{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d47edb02-7426-401b-9dc0-5eb045939e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e1551768-87d3-4f5b-9a21-8ddfa4baa785",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f0c32a08-9b5c-445a-9e8d-c08ee4af8318",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f681d9f-67ae-489f-b3e4-1e04c2da4a80",
   "metadata": {},
   "source": [
    "# 城市气候与海洋的关系数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a1865bd-2999-4146-a574-8922b048d825",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 1、导入数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d43332d-3d22-4da4-99e7-b4e05f2e3f61",
   "metadata": {},
   "source": [
    "### ① 方法一：用正则去匹配文件名"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909964fa-7b12-4351-b6da-850f5201102c",
   "metadata": {},
   "source": [
    "复习OS模块，写出10个常见的函数：\n",
    "- os.path.exists  # 判断文件是否存在\n",
    "- os.isdir        # 判断路径是否是个文件夹\n",
    "- os.isfile       # 判断路径是否是个文件\n",
    "- os.path.join    # 将文件路径和文件名字拼接在一起\n",
    "- os.path.split   # 将结合在一起的字符串，拆成路径和文件名\n",
    "- os.mkdir        # 创建文件夹\n",
    "- os.chdir        # 改变当前工作目录到指定的路径\n",
    "- os.path.basename # 获取文件路径\n",
    "- os.path.rename   # 修改文件名字\n",
    "- os.path.remove   # 删除文件 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f0ffe69-a48b-46f3-9ad7-46092ab100c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = os.listdir('dataset')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77ae4f45-fe61-440b-a906-739b49100969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['asti_150715.csv',\n",
       " 'asti_250715.csv',\n",
       " 'asti_270615.csv',\n",
       " 'bologna_150715.csv',\n",
       " 'bologna_250715.csv',\n",
       " 'bologna_270615.csv',\n",
       " 'cesena_150715.csv',\n",
       " 'cesena_250715.csv',\n",
       " 'cesena_270615.csv',\n",
       " 'faenza_150715.csv',\n",
       " 'faenza_250715.csv',\n",
       " 'faenza_270615.csv',\n",
       " 'ferrara_150715.csv',\n",
       " 'ferrara_250715.csv',\n",
       " 'ferrara_270615.csv',\n",
       " 'mantova_150715.csv',\n",
       " 'mantova_250715.csv',\n",
       " 'mantova_270615.csv',\n",
       " 'milano_150715.csv',\n",
       " 'milano_250715.csv',\n",
       " 'milano_270615.csv',\n",
       " 'piacenza_150715.csv',\n",
       " 'piacenza_250715.csv',\n",
       " 'piacenza_270615.csv',\n",
       " 'ravenna_150715.csv',\n",
       " 'ravenna_250715.csv',\n",
       " 'ravenna_270615.csv',\n",
       " 'torino_150715.csv',\n",
       " 'torino_250715.csv',\n",
       " 'torino_270615.csv']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "599edc5f-bae3-4f8f-a01a-c3d68e37e517",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<re.Match object; span=(0, 17), match='torino_270615.csv'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.match(r'^[a-z]+_\\d{6}\\.csv', 'torino_270615.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b81149f-352a-46b4-aac2-4b722c16bd9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.\\\\dataset\\\\asti_150715.csv',\n",
       " '.\\\\dataset\\\\asti_250715.csv',\n",
       " '.\\\\dataset\\\\asti_270615.csv',\n",
       " '.\\\\dataset\\\\bologna_150715.csv',\n",
       " '.\\\\dataset\\\\bologna_250715.csv',\n",
       " '.\\\\dataset\\\\bologna_270615.csv',\n",
       " '.\\\\dataset\\\\cesena_150715.csv',\n",
       " '.\\\\dataset\\\\cesena_250715.csv',\n",
       " '.\\\\dataset\\\\cesena_270615.csv',\n",
       " '.\\\\dataset\\\\faenza_150715.csv',\n",
       " '.\\\\dataset\\\\faenza_250715.csv',\n",
       " '.\\\\dataset\\\\faenza_270615.csv',\n",
       " '.\\\\dataset\\\\ferrara_150715.csv',\n",
       " '.\\\\dataset\\\\ferrara_250715.csv',\n",
       " '.\\\\dataset\\\\ferrara_270615.csv',\n",
       " '.\\\\dataset\\\\mantova_150715.csv',\n",
       " '.\\\\dataset\\\\mantova_250715.csv',\n",
       " '.\\\\dataset\\\\mantova_270615.csv',\n",
       " '.\\\\dataset\\\\milano_150715.csv',\n",
       " '.\\\\dataset\\\\milano_250715.csv',\n",
       " '.\\\\dataset\\\\milano_270615.csv',\n",
       " '.\\\\dataset\\\\piacenza_150715.csv',\n",
       " '.\\\\dataset\\\\piacenza_250715.csv',\n",
       " '.\\\\dataset\\\\piacenza_270615.csv',\n",
       " '.\\\\dataset\\\\ravenna_150715.csv',\n",
       " '.\\\\dataset\\\\ravenna_250715.csv',\n",
       " '.\\\\dataset\\\\ravenna_270615.csv',\n",
       " '.\\\\dataset\\\\torino_150715.csv',\n",
       " '.\\\\dataset\\\\torino_250715.csv',\n",
       " '.\\\\dataset\\\\torino_270615.csv']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files = [\n",
    "    '.\\dataset\\\\' + file_name for file_name in filenames if re.match(r'^[a-z]+_\\d{6}\\.csv', file_name)\n",
    "]\n",
    "\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695b7afe-e15a-41e0-bf93-911d58dfc1fe",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### ② 方法二：用string字符串方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "adc4480b-7cbf-4ed5-9c52-aa52895ee95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asti_150715.csv\n",
      "asti_250715.csv\n",
      "asti_270615.csv\n",
      "bologna_150715.csv\n",
      "bologna_250715.csv\n",
      "bologna_270615.csv\n",
      "cesena_150715.csv\n",
      "cesena_250715.csv\n",
      "cesena_270615.csv\n",
      "faenza_150715.csv\n",
      "faenza_250715.csv\n",
      "faenza_270615.csv\n",
      "ferrara_150715.csv\n",
      "ferrara_250715.csv\n",
      "ferrara_270615.csv\n",
      "mantova_150715.csv\n",
      "mantova_250715.csv\n",
      "mantova_270615.csv\n",
      "milano_150715.csv\n",
      "milano_250715.csv\n",
      "milano_270615.csv\n",
      "piacenza_150715.csv\n",
      "piacenza_250715.csv\n",
      "piacenza_270615.csv\n",
      "ravenna_150715.csv\n",
      "ravenna_250715.csv\n",
      "ravenna_270615.csv\n",
      "torino_150715.csv\n",
      "torino_250715.csv\n",
      "torino_270615.csv\n"
     ]
    }
   ],
   "source": [
    "for file_name in filenames:\n",
    "    if file_name.endswith('.csv'):\n",
    "        print(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "172b81e4-10b2-4092-b783-5f60db58c05b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['asti_150715.csv',\n",
       " 'asti_250715.csv',\n",
       " 'asti_270615.csv',\n",
       " 'bologna_150715.csv',\n",
       " 'bologna_250715.csv',\n",
       " 'bologna_270615.csv',\n",
       " 'cesena_150715.csv',\n",
       " 'cesena_250715.csv',\n",
       " 'cesena_270615.csv',\n",
       " 'faenza_150715.csv',\n",
       " 'faenza_250715.csv',\n",
       " 'faenza_270615.csv',\n",
       " 'ferrara_150715.csv',\n",
       " 'ferrara_250715.csv',\n",
       " 'ferrara_270615.csv',\n",
       " 'mantova_150715.csv',\n",
       " 'mantova_250715.csv',\n",
       " 'mantova_270615.csv',\n",
       " 'milano_150715.csv',\n",
       " 'milano_250715.csv',\n",
       " 'milano_270615.csv',\n",
       " 'piacenza_150715.csv',\n",
       " 'piacenza_250715.csv',\n",
       " 'piacenza_270615.csv',\n",
       " 'ravenna_150715.csv',\n",
       " 'ravenna_250715.csv',\n",
       " 'ravenna_270615.csv',\n",
       " 'torino_150715.csv',\n",
       " 'torino_250715.csv',\n",
       " 'torino_270615.csv']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for循环写法，变成列表推导式\n",
    "[file_name for file_name in filenames if file_name.endswith('.csv')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3a6750b-b480-4dce-83cf-5d14ec1a86e1",
   "metadata": {},
   "source": [
    "### ③ 方法三：使用glob模块"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c35ceb2-fe81-4e25-9a3f-f4e3efa6416d",
   "metadata": {},
   "source": [
    "glob()函数：这是glob模块中最常用的函数，它接受一个或多个字符串参数，这些字符串定义了文件名模式。模式中可以包含通配符来匹配文件名或路径中的部分字符。常见的通配符包括：\n",
    "\n",
    "- *：匹配任意数量（包括零）的字符。\n",
    "- ?：匹配任何单个字符。\n",
    "- [...]：匹配括号内列出的任何一个字符，例如[abc]匹配'a'、'b'或'c'。\n",
    "- **：在Python 3.5及以后的版本中，**可以匹配任意深度的目录结构，包括子目录和子目录下的文件。\n",
    "\n",
    "iglob()函数：类似于glob()，但它返回一个迭代器而不是列表，这对于处理大量文件时更节省内存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56604d3d-5e6a-47f4-9022-8b7c5e56e332",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59fb28a9-f7d9-4bd4-bfca-5651d17be1a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dataset\\\\asti_150715.csv',\n",
       " 'dataset\\\\asti_250715.csv',\n",
       " 'dataset\\\\asti_270615.csv',\n",
       " 'dataset\\\\bologna_150715.csv',\n",
       " 'dataset\\\\bologna_250715.csv',\n",
       " 'dataset\\\\bologna_270615.csv',\n",
       " 'dataset\\\\cesena_150715.csv',\n",
       " 'dataset\\\\cesena_250715.csv',\n",
       " 'dataset\\\\cesena_270615.csv',\n",
       " 'dataset\\\\faenza_150715.csv',\n",
       " 'dataset\\\\faenza_250715.csv',\n",
       " 'dataset\\\\faenza_270615.csv',\n",
       " 'dataset\\\\ferrara_150715.csv',\n",
       " 'dataset\\\\ferrara_250715.csv',\n",
       " 'dataset\\\\ferrara_270615.csv',\n",
       " 'dataset\\\\mantova_150715.csv',\n",
       " 'dataset\\\\mantova_250715.csv',\n",
       " 'dataset\\\\mantova_270615.csv',\n",
       " 'dataset\\\\milano_150715.csv',\n",
       " 'dataset\\\\milano_250715.csv',\n",
       " 'dataset\\\\milano_270615.csv',\n",
       " 'dataset\\\\piacenza_150715.csv',\n",
       " 'dataset\\\\piacenza_250715.csv',\n",
       " 'dataset\\\\piacenza_270615.csv',\n",
       " 'dataset\\\\ravenna_150715.csv',\n",
       " 'dataset\\\\ravenna_250715.csv',\n",
       " 'dataset\\\\ravenna_270615.csv',\n",
       " 'dataset\\\\torino_150715.csv',\n",
       " 'dataset\\\\torino_250715.csv',\n",
       " 'dataset\\\\torino_270615.csv']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob('dataset/*.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "666e9c58-5b65-41a8-a3d9-e61cb72d5a9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n",
      "['.ipynb_checkpoints', 'dataset']\n",
      "['climate_ocean_relationship.ipynb']\n",
      ".\\.ipynb_checkpoints\n",
      "[]\n",
      "['climate_ocean_relationship-checkpoint.ipynb']\n",
      ".\\dataset\n",
      "[]\n",
      "['asti_150715.csv', 'asti_250715.csv', 'asti_270615.csv', 'bologna_150715.csv', 'bologna_250715.csv', 'bologna_270615.csv', 'cesena_150715.csv', 'cesena_250715.csv', 'cesena_270615.csv', 'faenza_150715.csv', 'faenza_250715.csv', 'faenza_270615.csv', 'ferrara_150715.csv', 'ferrara_250715.csv', 'ferrara_270615.csv', 'mantova_150715.csv', 'mantova_250715.csv', 'mantova_270615.csv', 'milano_150715.csv', 'milano_250715.csv', 'milano_270615.csv', 'piacenza_150715.csv', 'piacenza_250715.csv', 'piacenza_270615.csv', 'ravenna_150715.csv', 'ravenna_250715.csv', 'ravenna_270615.csv', 'torino_150715.csv', 'torino_250715.csv', 'torino_270615.csv']\n"
     ]
    }
   ],
   "source": [
    "for x1, x2, x3 in os.walk('.'):\n",
    "    # 当前目录\n",
    "    print(x1)\n",
    "    # 当前目录下的所有子目录\n",
    "    print(x2)\n",
    "    # 当前目录下的所有普通文件\n",
    "    print(x3)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c70ab0d4-771c-4fe0-8be4-f8bbdbb75edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object _walk at 0x0000024A6E020AC0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.walk('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2cbeca41-b6ff-4d11-ab11-098b3b2e8e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "94e694a7-0a46-41c4-9015-cdb19efb1217",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.05</td>\n",
       "      <td>66</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863176</td>\n",
       "      <td>2.57</td>\n",
       "      <td>42.501</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 10:39:36</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.51</td>\n",
       "      <td>64</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>1.54</td>\n",
       "      <td>263.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.39</td>\n",
       "      <td>58</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>2.60</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.10</td>\n",
       "      <td>54</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.10</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.23</td>\n",
       "      <td>45</td>\n",
       "      <td>1016</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877645</td>\n",
       "      <td>2.10</td>\n",
       "      <td>120.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 14:40:45</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.95</td>\n",
       "      <td>46</td>\n",
       "      <td>1016</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436881329</td>\n",
       "      <td>2.10</td>\n",
       "      <td>110.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 15:42:09</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.31</td>\n",
       "      <td>46</td>\n",
       "      <td>1015</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436884929</td>\n",
       "      <td>2.10</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 16:42:09</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32.47</td>\n",
       "      <td>49</td>\n",
       "      <td>1015</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436888513</td>\n",
       "      <td>2.10</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 17:41:53</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34.28</td>\n",
       "      <td>46</td>\n",
       "      <td>1015</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436892132</td>\n",
       "      <td>1.50</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 18:42:12</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31.41</td>\n",
       "      <td>46</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436895723</td>\n",
       "      <td>2.10</td>\n",
       "      <td>240.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 19:42:03</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29.72</td>\n",
       "      <td>58</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436899336</td>\n",
       "      <td>2.10</td>\n",
       "      <td>70.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 20:42:16</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27.68</td>\n",
       "      <td>62</td>\n",
       "      <td>1015</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436902943</td>\n",
       "      <td>1.50</td>\n",
       "      <td>40.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 21:42:23</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26.44</td>\n",
       "      <td>69</td>\n",
       "      <td>1015</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436906532</td>\n",
       "      <td>1.50</td>\n",
       "      <td>30.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 22:42:12</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>26.17</td>\n",
       "      <td>69</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436910149</td>\n",
       "      <td>1.50</td>\n",
       "      <td>330.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 23:42:29</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>25.21</td>\n",
       "      <td>78</td>\n",
       "      <td>1014</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436913739</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 00:42:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>24.45</td>\n",
       "      <td>73</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436917311</td>\n",
       "      <td>2.60</td>\n",
       "      <td>350.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 01:41:51</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24.32</td>\n",
       "      <td>73</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436920931</td>\n",
       "      <td>2.60</td>\n",
       "      <td>340.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 02:42:11</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24.24</td>\n",
       "      <td>73</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436924426</td>\n",
       "      <td>2.10</td>\n",
       "      <td>360.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 03:40:26</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.00</td>\n",
       "      <td>73</td>\n",
       "      <td>1016</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436928078</td>\n",
       "      <td>1.50</td>\n",
       "      <td>310.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 04:41:18</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.00</td>\n",
       "      <td>83</td>\n",
       "      <td>1017</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>1436931718</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 05:41:58</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.00</td>\n",
       "      <td>78</td>\n",
       "      <td>1017</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>1436935298</td>\n",
       "      <td>3.10</td>\n",
       "      <td>350.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 06:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25.00</td>\n",
       "      <td>73</td>\n",
       "      <td>1017</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436938882</td>\n",
       "      <td>1.50</td>\n",
       "      <td>330.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 07:41:22</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>27.00</td>\n",
       "      <td>65</td>\n",
       "      <td>1017</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436942516</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 08:41:56</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27.00</td>\n",
       "      <td>65</td>\n",
       "      <td>1017</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436945951</td>\n",
       "      <td>2.10</td>\n",
       "      <td>50.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-15 09:39:11</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     temp  humidity  pressure       description          dt  wind_speed  \\\n",
       "0   28.05        66      1014      Sky is Clear  1436863176        2.57   \n",
       "1   29.51        64      1014      Sky is Clear  1436866759        1.54   \n",
       "2   30.39        58      1017      Sky is Clear  1436870510        2.60   \n",
       "3   31.10        54      1017      Sky is Clear  1436874098        2.10   \n",
       "4   33.23        45      1016        few clouds  1436877645        2.10   \n",
       "5   32.95        46      1016        few clouds  1436881329        2.10   \n",
       "6   34.31        46      1015        few clouds  1436884929        2.10   \n",
       "7   32.47        49      1015        few clouds  1436888513        2.10   \n",
       "8   34.28        46      1015        few clouds  1436892132        1.50   \n",
       "9   31.41        46      1014      Sky is Clear  1436895723        2.10   \n",
       "10  29.72        58      1014      Sky is Clear  1436899336        2.10   \n",
       "11  27.68        62      1015      Sky is Clear  1436902943        1.50   \n",
       "12  26.44        69      1015      Sky is Clear  1436906532        1.50   \n",
       "13  26.17        69      1016      Sky is Clear  1436910149        1.50   \n",
       "14  25.21        78      1014      Sky is Clear  1436913739        0.51   \n",
       "15  24.45        73      1016      Sky is Clear  1436917311        2.60   \n",
       "16  24.32        73      1016      Sky is Clear  1436920931        2.60   \n",
       "17  24.24        73      1016      Sky is Clear  1436924426        2.10   \n",
       "18  24.00        73      1016      Sky is Clear  1436928078        1.50   \n",
       "19  23.00        83      1017     broken clouds  1436931718        0.50   \n",
       "20  23.00        78      1017  scattered clouds  1436935298        3.10   \n",
       "21  25.00        73      1017        few clouds  1436938882        1.50   \n",
       "22  27.00        65      1017        few clouds  1436942516        0.50   \n",
       "23  27.00        65      1017      Sky is Clear  1436945951        2.10   \n",
       "\n",
       "    wind_deg  city                  day  dist  \n",
       "0     42.501  Asti  2015-07-14 10:39:36   315  \n",
       "1    263.000  Asti  2015-07-14 11:39:19   315  \n",
       "2    100.000  Asti  2015-07-14 12:41:50   315  \n",
       "3     90.000  Asti  2015-07-14 13:41:38   315  \n",
       "4    120.000  Asti  2015-07-14 14:40:45   315  \n",
       "5    110.000  Asti  2015-07-14 15:42:09   315  \n",
       "6    100.000  Asti  2015-07-14 16:42:09   315  \n",
       "7    100.000  Asti  2015-07-14 17:41:53   315  \n",
       "8    100.000  Asti  2015-07-14 18:42:12   315  \n",
       "9    240.000  Asti  2015-07-14 19:42:03   315  \n",
       "10    70.000  Asti  2015-07-14 20:42:16   315  \n",
       "11    40.000  Asti  2015-07-14 21:42:23   315  \n",
       "12    30.000  Asti  2015-07-14 22:42:12   315  \n",
       "13   330.000  Asti  2015-07-14 23:42:29   315  \n",
       "14     0.000  Asti  2015-07-15 00:42:19   315  \n",
       "15   350.000  Asti  2015-07-15 01:41:51   315  \n",
       "16   340.000  Asti  2015-07-15 02:42:11   315  \n",
       "17   360.000  Asti  2015-07-15 03:40:26   315  \n",
       "18   310.000  Asti  2015-07-15 04:41:18   315  \n",
       "19     0.000  Asti  2015-07-15 05:41:58   315  \n",
       "20   350.000  Asti  2015-07-15 06:41:38   315  \n",
       "21   330.000  Asti  2015-07-15 07:41:22   315  \n",
       "22     0.000  Asti  2015-07-15 08:41:56   315  \n",
       "23    50.000  Asti  2015-07-15 09:39:11   315  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(files[0], index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2664f13-68ef-4604-b0f9-79bf590be0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把一个文件读取进来，拼接在一起\n",
    "# append, concat\n",
    "# 创建一个空的dataframe, 来和读进来的dataframe进行拼接\n",
    "cities = pd.DataFrame()\n",
    "\n",
    "for file_name in files:\n",
    "    temp = pd.read_csv(file_name)\n",
    "    cities = pd.concat((cities, temp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "77293b6c-d86d-4dc1-8cc0-68ba8da084cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(675, 11)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看行数\n",
    "cities.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97b88442-2cb7-479b-81b9-a6a7ed4b2941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>28.05</td>\n",
       "      <td>66</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863176</td>\n",
       "      <td>2.57</td>\n",
       "      <td>42.501</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 10:39:36</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29.51</td>\n",
       "      <td>64</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>1.54</td>\n",
       "      <td>263.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.39</td>\n",
       "      <td>58</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>2.60</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>31.10</td>\n",
       "      <td>54</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.10</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>33.23</td>\n",
       "      <td>45</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877645</td>\n",
       "      <td>2.10</td>\n",
       "      <td>120.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 14:40:45</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0   temp  humidity  pressure   description          dt  \\\n",
       "0           0  28.05        66    1014.0  Sky is Clear  1436863176   \n",
       "1           1  29.51        64    1014.0  Sky is Clear  1436866759   \n",
       "2           2  30.39        58    1017.0  Sky is Clear  1436870510   \n",
       "3           3  31.10        54    1017.0  Sky is Clear  1436874098   \n",
       "4           4  33.23        45    1016.0    few clouds  1436877645   \n",
       "\n",
       "   wind_speed  wind_deg  city                  day  dist  \n",
       "0        2.57    42.501  Asti  2015-07-14 10:39:36   315  \n",
       "1        1.54   263.000  Asti  2015-07-14 11:39:19   315  \n",
       "2        2.60   100.000  Asti  2015-07-14 12:41:50   315  \n",
       "3        2.10    90.000  Asti  2015-07-14 13:41:38   315  \n",
       "4        2.10   120.000  Asti  2015-07-14 14:40:45   315  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据\n",
    "cities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb36d3eb-5b6d-4a84-9704-2646a7296cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除没用的列\n",
    "cities.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a9cdae8c-8721-4ae5-be4f-52239d0c23ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.05</td>\n",
       "      <td>66</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863176</td>\n",
       "      <td>2.57</td>\n",
       "      <td>42.501</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 10:39:36</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.51</td>\n",
       "      <td>64</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>1.54</td>\n",
       "      <td>263.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.39</td>\n",
       "      <td>58</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>2.60</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.10</td>\n",
       "      <td>54</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.10</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.23</td>\n",
       "      <td>45</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877645</td>\n",
       "      <td>2.10</td>\n",
       "      <td>120.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 14:40:45</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp  humidity  pressure   description          dt  wind_speed  wind_deg  \\\n",
       "0  28.05        66    1014.0  Sky is Clear  1436863176        2.57    42.501   \n",
       "1  29.51        64    1014.0  Sky is Clear  1436866759        1.54   263.000   \n",
       "2  30.39        58    1017.0  Sky is Clear  1436870510        2.60   100.000   \n",
       "3  31.10        54    1017.0  Sky is Clear  1436874098        2.10    90.000   \n",
       "4  33.23        45    1016.0    few clouds  1436877645        2.10   120.000   \n",
       "\n",
       "   city                  day  dist  \n",
       "0  Asti  2015-07-14 10:39:36   315  \n",
       "1  Asti  2015-07-14 11:39:19   315  \n",
       "2  Asti  2015-07-14 12:41:50   315  \n",
       "3  Asti  2015-07-14 13:41:38   315  \n",
       "4  Asti  2015-07-14 14:40:45   315  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6898af69-8af8-4886-baab-2e28e3ae191e",
   "metadata": {},
   "source": [
    "## 2、数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "89d87877-9d34-44ea-a8c4-376e8e05cee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 查看距离的分类情况\n",
    "dist = cities['dist'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b03b55e2-f4b0-4a82-9954-930570212a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([315,  71,  14,  37,  47, 121, 250, 200,   8, 357], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1745d1d4-7140-4c77-a1ba-7eb1015025d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Asti        34.31\n",
       "Bologna     33.85\n",
       "Cesena      32.81\n",
       "Faenza      32.74\n",
       "Ferrara     33.43\n",
       "Mantova     34.18\n",
       "Milano      34.81\n",
       "Piacenza    33.92\n",
       "Ravenna     32.79\n",
       "Torino      34.69\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照城市分组，并查看该城市里的所有温度中的最大值\n",
    "cities.groupby(by='city')['temp'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6bf854ec-4a56-4e07-943f-d1466f66bf87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 根据城市去分组\n",
    "temp_max = cities.groupby(by='city')['temp'].max().values\n",
    "temp_min = cities.groupby(by='city')['temp'].min().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3052f64a-d991-4bca-9f3f-2e237353d112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.31, 33.85, 32.81, 32.74, 33.43, 34.18, 34.81, 33.92, 32.79,\n",
       "       34.69])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([18.01, 18.44, 18.41, 18.62, 18.81, 19.03, 18.28, 18.68, 18.22,\n",
       "       18.94])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(temp_max, temp_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5ca85b9b-17ce-4494-939b-15fd58160f37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Asti         93\n",
       "Bologna      92\n",
       "Cesena      100\n",
       "Faenza      100\n",
       "Ferrara      85\n",
       "Mantova     100\n",
       "Milano      100\n",
       "Piacenza    100\n",
       "Ravenna      94\n",
       "Torino       88\n",
       "Name: humidity, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照城市去分组，并查看城市里面的所有湿度中的最大值\n",
    "cities.groupby(by='city')['humidity'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3029f1dd-4a1a-4d50-9727-199b28d50ad3",
   "metadata": {},
   "outputs": [],
   "source": [
    "humidity_max = cities.groupby(by='city')['humidity'].max().values\n",
    "humidity_min = cities.groupby(by='city')['humidity'].min().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "643a06b0-64a1-4dbe-b5ff-f8bed5a19634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 93,  92, 100, 100,  85, 100, 100, 100,  94,  88], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([29, 40, 61, 61, 39, 28, 35, 35, 34, 45], dtype=int64)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(humidity_max, humidity_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e96b0058-e872-40e3-b7a0-ece40436b44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示最高温度与离海远近的关系\n",
    "# 查看两个一维的数据的关系，用散点图\n",
    "plt.scatter(x=dist, y=temp_max)\n",
    "plt.title('显示最高温度与离海远近的关系')\n",
    "plt.xlabel('离海岸距离')\n",
    "plt.ylabel('最高温度')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cefce2-1aad-4fac-9fda-1115b282c437",
   "metadata": {},
   "source": [
    "观察发现，离海近的可以形成一条直线，离海远的也能形成一条直线"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736b1ce1-c7ba-4a03-8c6f-65c7762ff204",
   "metadata": {},
   "source": [
    "首先使用numpy把列表转换为numpy数组，用于后续计算"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6046a55c-c85d-4ff6-9428-98e2f8582c7d",
   "metadata": {},
   "source": [
    "分别以100公里和50公里为分界点，划分为离海近和离海远的两组数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d105cb62-081f-4d36-8d08-487dbc35ef0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71, 14, 37, 47,  8], dtype=int64)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond_near = dist < 100\n",
    "dist_near = dist[cond_near]\n",
    "dist_near"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "055a5c92-ad96-42d1-b9ce-dfc0ff68cb0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([315,  71, 121, 250, 200, 357], dtype=int64)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cond_far = dist > 50\n",
    "dist_far = dist[cond_far]\n",
    "dist_far"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e9d7cd7-f74e-4f89-8309-9f62b1b61f59",
   "metadata": {},
   "source": [
    "风向和风速的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "b8c339d6-0169-431d-8f2e-4e110f849b71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.05</td>\n",
       "      <td>66</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863176</td>\n",
       "      <td>2.57</td>\n",
       "      <td>42.501</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 10:39:36</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.51</td>\n",
       "      <td>64</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866759</td>\n",
       "      <td>1.54</td>\n",
       "      <td>263.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 11:39:19</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.39</td>\n",
       "      <td>58</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870510</td>\n",
       "      <td>2.60</td>\n",
       "      <td>100.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 12:41:50</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.10</td>\n",
       "      <td>54</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.10</td>\n",
       "      <td>90.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.23</td>\n",
       "      <td>45</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>few clouds</td>\n",
       "      <td>1436877645</td>\n",
       "      <td>2.10</td>\n",
       "      <td>120.000</td>\n",
       "      <td>Asti</td>\n",
       "      <td>2015-07-14 14:40:45</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp  humidity  pressure   description          dt  wind_speed  wind_deg  \\\n",
       "0  28.05        66    1014.0  Sky is Clear  1436863176        2.57    42.501   \n",
       "1  29.51        64    1014.0  Sky is Clear  1436866759        1.54   263.000   \n",
       "2  30.39        58    1017.0  Sky is Clear  1436870510        2.60   100.000   \n",
       "3  31.10        54    1017.0  Sky is Clear  1436874098        2.10    90.000   \n",
       "4  33.23        45    1016.0    few clouds  1436877645        2.10   120.000   \n",
       "\n",
       "   city                  day  dist  \n",
       "0  Asti  2015-07-14 10:39:36   315  \n",
       "1  Asti  2015-07-14 11:39:19   315  \n",
       "2  Asti  2015-07-14 12:41:50   315  \n",
       "3  Asti  2015-07-14 13:41:38   315  \n",
       "4  Asti  2015-07-14 14:40:45   315  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "422206c1-797c-4c34-9ff5-37ff1daaec42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wind_deg', ylabel='wind_speed'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities.plot(x='wind_deg', y='wind_speed', kind='scatter')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1c7fc3-d204-4ff7-b81e-48d8a63c91b6",
   "metadata": {},
   "source": [
    "在子图中，同时比较风向与湿度和风力的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "dd65fafd-6c5b-439b-acb5-b2e54f651b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'wind degree and humidity')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B5OEJv386b4Juf+mVrXbMrIxuIns29Zi/chfmrtiFxzbXY+6KXfjyyl29x0qLCvDxy0oSzv/4ZSW9/Z/u+8MMpPWF6UjmRPcD2CCE2KJ1TnV1taitrXVNBoZhGDchoj1CiGqv5UgXZsbs+uY2XL98W0r6xsXTUFlWjJb2MKYs24wLXRdnjfrk5WDHkhm+eEHKaGkP40TreQwr6Wta3tpjLdh29DSmjRrUq2AZyVtJL8wPoaMzknC8vrkNdQ1nMKFiYIqypXdMhtVr7bSPFZySU01uWX8ZbXsAlvvBSWTPph61x1owd8WulPQ1t09C9chSw3mn+/5Q0BuzXfM+QUQ5AK4TQtyjcmwRgEUAMHy49lcUwzAME1y07CW3159GR2cEZ893Ii8nBxdwUSlWbAuDoBSXFhVYlrN6ZKmqciXLW69MZXbS7DEZVq+10z5WcEpONbll/WWm7b1UhhX0bKFl8unZxVePLDWcd7rvDyO46ZJtKoDdageEEI8jZmdcXV3t3lQ1wzAM4xla9pL3v3QYBbkhdEYi6El6A/jFtpBhMhk7tubTRg3CY5vrVdPt5u01btoU3wggdf6cYRiGyQrUbCpzCOiMCLSFuxHuFhBCoCDXf7aFDJPJ2LE1l9nF28nba1y1KTYC2xQzDBNk2KZYjmJT2ScvB9/940G0hbt7jxUX5OKXX5qIAX3z0m5byGQ+XtmtBgU7tuZ27Ky9xBObYoZhGIYBLtpUtrSHVV0xjbm0PyssjOP4NWqan7Bja27HztqvcEQ7hmEYJi342RUTk1n4OWoa4194pphhGIZJG7dMGIoplYN4SZtxFSVqWlA9mzDewDPFAUIv6pObEaHsRLxhGDcJWiQ0P0BEI4noRSJ6lYh+Gkt7goheI6LvpUOG0qICjK8YyMoJ4xp+jprG+BeeKQ4IerZRbtpNLa05gFW7jvf+XjB5OO69dawjeTOMHdhe0DLLANwnhNhFRM8Q0ecAhIQQ1xDRr4holBDiqNdCMowdFFOdu5LGCP4QY/RgpTgAxNtGKUtBdz23H1Mqoz4BtY7Zffjrm9sSFGIAWLXzOBZMGhE443kms9B7JvilJ+VyAHtjf78P4KcAvhH7vRnAtQBYKWYCD5vqMGZh84kAoNhGxaPYRukds4teVBqG8RI37/ssYA2AHxDRZwB8ClFFuDF27ByAsuQLiGgREdUSUe2pU6dsFc4mL+bg9rKH16Y63H/BgmeKA4DMNsotu6kgR6VhMhu2F7SOEOLHRHQtgO8A+B2AwQCUhiuCymSJU1FI2eTFHNxewYb7L3jwTHEA0HNj5KaLoyBHpWEyG3btZZs6AMMBPAJgD6ImEwAwHsA7bhTILrLMwe0VbLj/ggnPFAcEPdsoN+2m7r11LBZMGuHLqDRMdsP2grb4DoBHhBAfEFENgFeJ6FIAnwYwyY0C2UWWObi9gg33XzBhpThAKDPDZo/ZJYhRaZjswM37PpMRQvwg7u9zRDQdwA0AHhRCnHW6vPrmNrxx/G8Id3cnpCsmL16Gg/VjGGBZe1nNM7mNnar7pkNNWH+oGbOqyjCzqtxyPulGq/5O3I9qJl4fhLtV+09Wnl/DJWcirBQzDMNkOUKIVgCr3cg72a0jASgqyO21sfzZxrc9c/voR5tPWXtZUV7VXGteddkljtR91vKteLu5AwDwTO0JjC4rxCuLp5vOJ91o9b1TbkgVE69//UNdb1oEwOzHtmHnPTf0psnKY7eo6YVtihmGYRhXUHPrKAA88Lmx2LFkBqqG9Fd1+5iOQEF+tPmUtZcVpVXLteZda+zXfdOhpl6FWOFIcwc2HWoyLWc60er72mMtjt6PPUkzxQBw8lwnavY2ANDuG6U82XHGeVgpZhiGYVxBy33jha4ISosKPHX76Ee3frL2cjJPSvptpe7rDzWbSvcLWn2/7ehp1fOt3o/rDqh/HCjpsvuf3aKmH1aKGYZhGFeQuXX00u2jH936udEeWtcm+9WzUvdZVSkurXXT/YJW308bNUj1fKvtP3usun31kAEFuvn64fnIVlgpZhiGYVxB5tbRS7ePfnTr50Z7aOX50Fz7dZ9ZVY7RZYUJaaPLCn2/2U6r76tHljra/nMmVuBDRXkp6c/uaURLe9jXz0e2QkJY9sPuCNXV1aK2ttZTGRiGYaxCRHuEENVey5EurIzZft5d71fvE063B3ufSMVN7xMK+xrO4LYVO3Gh++LMdHFBLp5aeDXGx2Z8/fx8ZCJ6YzYrxQzDMDZgpTi78aNSzfiHlvYwpizbjAtdF5XiPnk52LFkBt8vHqE3ZrNLNoZhGIaxgB9dujH+QjHVuCvpPmGF2J+wUswwDMMwJol366VELbvruf2YUjmIFR4mAY6+GRx4o10SLe1h7Gs448v45F7JtulQE5as2ed735MMwzDpwo8u3czi5/edQu2xFjyy/ghqj7V4LYotSosKML5iICvEPodniuPw81KYV7IFNVoRwzCMm/jRpZsZ/Py+U5i/che210eV4cc212NqZSmeXDjJY6mYTIZnimP4MbqR17IFNVoRwzCM2/jRpZtR/Py+U6g91tKrECu8Wt8S+Bljxt/wTHEMZSlMsQ0DLi6FeT3IeSWbXrSiILndYRiGcYOg2or6+X2noBVdbtvR06geWWopTy88hWS6d5JMqx8rxTH8vBTmlWyzqsrwTO0J1XSGYRgmOmMcNGXAz+87hWmjBuGxzfWq6VbwwlwkCCYqdsjE+rH5RAw/L4V5JVtQoxUxDMMw2vj5fadQPbIUUysTZ4SnVpZamiX2wlwkCCYqdsjU+rk6U0xEvwLwJyHEC26W4xR+XgrzSrZXFk8PbLQihmEYRh0/v+8Unlw4CbXHWrDt6GlMGzXIstmEF+YiQTBRsUOm1s81pZiIpgIoD4pCrODnpTCvZJtZVc7KMMMwlrEbpjYodotm5ExuE7NtZLZN1M7XeqcknysrywnFVSG5HapHXpwdtnofeGEuolVmV3cEa2obAh+y2Uib2nluvQpt7YpSTER5AH4D4CUiulUIsdaNchiGYRh/s7TmAFbtOt77e8Hk4bj31rGGrw+K3aIZOZPb5PKywgRPP7I2MtsmZs5PPnfeVcOwes8JzWuddJumd6/YuQ9KiwpQfVlJgjeLj19W4uoHVmlRAeZVD8OqnRfrc9klfTF3xa7e32afBT8hi9Rnp7/sjhl2cMumeAGAQwAeBPAJIvpm/EEiWkREtURUe+rUKZdEYBiGYbykvrkt4eUGAKt2Hkd9c5uh64Nit2hGTrU2SXZ9qddGZtvEzPlq567adVzzWifdpundK3bvg/rmNlU5jd6HVmhpD2N10kb1Iyb6OQjcMmEodiyZgacWXo0dS2b0Kr12+svumGEXt5TijwF4XAjRBOApANfFHxRCPC6EqBZCVA8ePNglERiGYRgvqWs4o5q+vf50QiQ1rchqTkSNS0fUNjNyarWJ0fPMtone+clto3ZuMvFl6blN0yK5TOX39nr1a+oazti+D7TaUtYXdu4dI21pRAa/oxapz05/We0rp3DLprgewIdjf1cDeNelchiGYRifMqFioGr6/S8dRkFuSLo8b9cWNF2mF2bk1GoTo+eZbROt8w82nsVtj+9MaJsplYNSzk0mviyzbtNSTDOqh2F1bbTvw90R1WsmVAxESWG+rftAqy31+sLuvaPW7mZlCCp2nlsrfeUkbs0UPwHgOiLaBuCfATzsUjkMwzCMT6ksK8aCycMT0nII6IwIQ8vzdlyHpdP0woycam2S7PpyweThmpuLzLaJ2vnfv7kK9714KKVtAKScu2DycM2yzLhNUzXN2Hmx7zsjAjmUeI3SDnZdyKm1uV4bO3HvqMmc3FZ6MgQZO/1ltq+choQQaSlIi+rqalFbW+upDAzDMFYhoj1CiGqv5UgXVsZsZSd5n7wcfPePB9EW7tY8t7ggF08tvBrj42aGrOxi39dwBvNX7k4oSy1vJwmK94kTred128YN7xNq/ZFMcUEuHvjclbjQ1aPaDna9kBhtYyfvnWSZvfKq4AV+9T6hN2ZzRDuGYRjGVSrLilFZVoyW9rCp5XkFxXVYfXMbtrz1vqEXpRduuMy4zVTaROu3FvHKghkFLVk2vbZJPldWr3i3aVoYMScId0fw0fL+urPkdjxGGG1jJ++dZJmNypAJ2Okvr9qJI9oxDMMwaUFtWVVveT6epTUHcP3ybbhzzX5cv3wblq49YLosv0VtM4vZNtDCi7bR6/uCUNRuIieHMPsX2/F8XaNrcliVNej3DmMMNp9gGIaxAZtPmMfs8nx9cxuuX74tJX3j4mnS2aSgBP6QYacNtPCibdTMCW76+XZ0dl+cme2Tl4MdS2Z43l+Zcu8wibD5RBZQs7cB6w40YfbYcsyZWJFyXGafoxfK2c82UH6WjWH8CBGVAHgaQDGAN4UQXyeiJwBcAeAlIcSP0y2TbJlVz02T7Lk3GrUtnVgp204baOFFlNTkMjs6IygI5SQoxX4JF+znCLeMO7BSnCbcHIAn3b8BTec6AQAbD7+PZS+/hZ333NB7XBYdZtbyrb3O45+pPYHRZYV4ZfF0Q9d6iZ9lYxgf82UATwkh/oeIniaiuwCEhBDXENGviGiUEOKoW4VbcXXltJsmL6PkWS3ba1dVbuGF7TfDaME2xWlgbV0jpizbjPkrd2PKss2O2kvV7G3oVYgVTp7rRM3eBgDy6DCbDjWlRFM60tyBTYeaPI8so4efZWMYn9MCYDQRDQRQAWAEgNWxY5sBXOtawRZdXTnppsnLKHl2yvbaVZVbsP0u4yd4pthl4gfBC4h+Dd/13H5MqRzkyEO/7kCTZvqciRXSJbf1h5pVj68/1IyPj7hE91ovcWMpkWGyhO0AbgbwrwDeAlAAQPlSPwegMvkCIloEYBEADB8+PPmwYZRIV8pYCBhfKr/31rFYMGmEbXMpOzLYxW7ZTrWB37hlwlBMqRzE9ruM5/BMscs4EaZUj9ljy3XTZUtus6rKVI/Pqirz9XKdn2VjGJ9zP4CvCyHuRVQp/nsAylp1EVTeC0KIx4UQ1UKI6sGDB1su2O5SeWVZMeZWV9hSBp1crq891oJH1h9B7bEWy2V3RsyVbbQN6pvbsKa2wdLqWTpCYyejFi6Y8R4v7gUvYaXYZdy2l5ozsQJD+ucnpA3pn9+72U625DazqjwlmtLoskLMrCr39XKdn2VjGJ/TD8BYIgoBuBrAT3DRZGI8gHfcKtgPS+VOyTB/5S7MXbELj22ux9wVu/DllbsMl50Xuhi6LdLTgx31p03XQw87rtvcNPdjgkU23gvski0NPF/XiLtc3tTB3if8JxuTHQTNJRsRfQLAfwG4DMBOAJ8H8CqATQA+DWCSEOKs1vVuuGTzAjsy1B5rwdwVqUrwmtsnSYNYtLSHcc1PNiHcffHd66QLMrvu66Ys24wLXf5zj8akl0y+F9glm8ekw15qzsQKVWVYQRYdZmZVeYoybPRaL/GzbAzjR4QQfwYwJj6NiKYDuAHAg3oKsVWSP7qturpyUpm2425r21H1md1tR08nKMVq8p5oPY/8UAjh7oshhNXsimUf/FrH7ey30LJ5fvO9cxjQN89ze18/fEy5gR/rZcT+3Yh/cauTVl5NeLFSnCbY3yHDMH5FCNGKix4oHEXP5aMZvHSjlsy0UYPw2OZ61XQFLXmNmNTJ3E3qHbez30JNtvNd3fjaqlrkh7xtdz/1v5P4tV6y+1Qmtx2XqV66W2WbYoZhGMYV9Fw+msFLN2pqDOyXr5uuJ6/MplnmblJ23M5+i2TZCnIJRIRwt7ft7rf+dwo/10vvPpXJbcdlqtfuVnmmmGEYhnEFPZePWuZaaugt5bZ2dFpaZrWzZC0zUZAtPeuZ1Gnlvb3+NDo6I3jjeKtu2YA9123xsp0934lvPP0GuiL6ph5u0tIexpa33kduDiWk+yXqnR28dA9oBK37VCa3UROe2mMt2Hb0NKaNGtRrdmT0WrfMK1gpZhiGYVxhVlUZnqk9oZpuBq2l3Kd3v4vVcfkbXWa1u2QtM1EwYiKhZVKnlff9Lx1GQW4I4e6IIZns7LdQZGtpD3sabU7ppxAROjoT650JUe+CEM1P7T6VyW3EhGf+yl3YXh91ZfjY5npMrSzFkwsnGbrWTfMKNp9gGIZhXOGy0kJT6VqoLeXeccPlCQoxYGyZ1Ykla5mJgh23b2p55xDQGRFoC3ejMyKQNGnqmjtKL13oxfdTvEJcWBDKmKh3fnBRaAW7ctcea+lViBVerW9B7bEW6bPltnkFzxQzDMMwruBk5Mnkpdwtb71vKW+nlqxlJgp2vA7F590nLwff/eNBtIUvmjAU5ufigc9diQtdPa7vzvcq2pxaPxXmh/Cjz4zBdR/9kO8VR6MENZqfFRMg5dmUeW/Re7bcjmbLSjHDMAzjClpLoSNK+2FfwxnTSkD8Uq5VLwtOLlnLTBTseB1S8tYyYZj8kUGm825pD+PN984CIIy5tL/h673wnqTWTxEhMkohVgiqdyqzJkBKuhHvLVrPltvRbNl8gmEYhnEFtaXQqZWlmP/bP9uOkmXVy0LQlqxLiwowr3pYQtq86mGm5V1b14hJD2zCgt++jgW//TOuvn+jryOUBa2fmIvIns3qkaWYWpkY5GZqZak08I2RvO3CEe0YhmFsELSIdnaxMmYrO8VHlPbD/N/+2dEoWVZ3ofsxYIIaTkQWi0bR24xwd+LMa0Eu4bW7Z/q+/kHoJyYV2bOp5n3Cqbz14Ih2DMMwjGcoS6H7Gs4gRNZda6kpSDITBq2XpxNL1nZe6kZxwgb6ROt5hJJ35wEIUTQf5RynFU8nFNqgmhYw8mezeqSx2WEreVuFleI0wV+75vBLe/lFDobJBA42nrXsWsuKGzU3XTdpuZRyGidsoIeV9EWkJ3VVOCJ6cLDxLG57fKfjEdX8GqmNYfRgm+I0sLauEVOWbbZtQ5ct+KW9/CIHw2QCLe1h3PfioZT0799cJf3gtOJGzU3XTXoupZzGCdva0qICPDR3HPJCF2eLc3OApbPH4L4XDzkeUc3PkdoYRg+eKXaZ+MFBWf6667n9mFKZunPYzaU42YynW9FhzGKmvbJBDobJFFRdbBWEcOXQAZaulZkQuOm6SeZSymmccNul5BHvfcKtiGp+j9TGMFqwUuwyRgcHN5fiZMtYbi4xmsUvg6lf5GCYTKClPYyz57vQGUlysdUjMKykr/Sjvas7gvPd2mYXatfLXDfZMY0y4lIK0J/o2HSoCesPNWNWVZlqyOtk+YzY1sZfA6TaCZcWFWDa5R9KuMaNiGpBiNSmR31zG7bXn8agogJM/kgpj/lZBCvFLmNkcNBbirM76yCb8dRaYlwwaYQnM8Z+GUz9IgfDBJ34j/JITw/yQoQ+uaHeD/Tt9adNfbTn5gC5oYsmBFof/YrrplU7Ez/4K8uKbdu7Ki6lXo0bt5NdSulNdMxavhVvN3cAAJ6pPYHRZYV4ZfF01TYzKl/8NRe6IxBCoG9eru71pUUFmHfVsIT2teLuTS3fBz8/Dncl1SEIymXy/RbKISyfN57tobMEx5ViIsoF8NfYPwD4phDigNPlBAUjg4ObS3GyGU+3o8OYxS+DqV/kYJggo/ZRXpAL/PJLH8OYS6NmE4q7MaMf7d09wB++9glUjyyVfvSrRcZyyjTqyYWTNGeC9SY6zp7v6lWIFY40d2DToSbMrCq3JJ/aNQB6o+BpXd/SHsbqPYmhslfXnsC/zbzc9lgXxEhtavdbpEfgO2vYdC5bcGOmeByA3wshlriQdyCRDQ5Gl+KsIJvxdDs6jBX8Mpj6RQ6GCSpqH+X5oRAG9M1HaVEB9jWcsfTR/k7LB6geWWrIzCnZdZMZ0yjZXgstl1J6Ex3N5y6oHlt/qBkzq8otmW6pXROP1vVaZe38SwsudEVs7zEJmjs1rfsNgC3TuSB7MdIzAZLVS7ZPqmZvA9YdaMLsseWYM7Ei4ZibPo71cEMpngTgs0Q0BcC7AP6fEKJbck3Gozc4GFmKs1Ou3oyn3hKjl/hlMPWLHAwTRIaV9O2drVRoC3f3fpQPK+mLjs7E4x2d3YY/2oeV9MWFJFvjC90RXTMno6ZRdvZa6E10nD3fhWdqT6Qcm1VV1itfe1Kbtce1mRpqdYonvk1l17WHu/Evv3+j97eXe0zSjd5kkFXTuSC7ptMzAZLVS7ZPatL9G9B0rhMAsPHw+1j28lvYec8NAOTPnpt7sNxwyfY6gE8KIa4FcAbATcknENEiIqolotpTp065IELweHLhJKy5fRL+dUYl1tw+yVF/l7dMGIodS2bgqYVXY8eSGSkP5L23jsXGxdPw8Nxx2Lh4WtYMgAzDuMuf9r+nm97a0Ylk97k9IpoOGAvpmhyVVRal1YiLM7vu3PTC2M6sKsfossKEY6PLCns32x071Y7kGohYupE69csPpRyPb1Ot64oLcpEfQkrZTrmxCwJq91soh/DQXGumc0F2TadnAiSrl8xlYc3ehl6FWOHkuU7U7G2QPntuu0N0Y6Z4vxBC6fG3AIxKPkEI8TiAx4FoyFAXZAgkdqK7yJDNeLoVHYZhmOxl7f6TmunzrxlpaE+Dml2wwonW8+ibl5swG903L1e61C0zjXJir4WezfEri6drep+wusdEqdPvXntHdZZaS/b4tnjj+N/wwxcOG742E1HuNye8TwTZi5HefZiXG9Ktl+weXnegSfX4ugNN6NZY8FDuQbfdIboxU/wkEY0nohCAzwLY50IZDMMwjM+5ddwQ3XSjexoqy4oxt7oiRTEzYgpR39yGNbUNKbOdpUUFGB8rZ1/DmYTZOyNytbSHU65LpnpkKe6YNVr1ZT2zqhzL5o5PccemtZfEyB6T0qIC3DL+UqnsateNrxiIaysHm742E6ksK8ZXpozE7PGX2lJeg+zFSO8+lNVLdg/PHpvqglBJlz17dp4PI7ihFN8L4EkAdQB2CiE2ulAGwzAM43PmXzMSA/okLucP6BPC/GtGAjBmHqGHzBRiac0BXL98G+5csx/XL9+GpWsTHSFpRa2UyeVmtEs90wsj2GnTN0+eQyiHEtL8sMckqDgRjdAr9O5DWb1k9/CciRUY0j8/4fiQ/vmYM7FCev/afT5kkMz+ym2qq6tFbW2tpzIwDMNYhYj2CCGqvZYjXVgZs5967RjW7j+JW8cN6VWI47EbUVNtF3x9cxuuX74t5dyNi6f1umZT3MEp9MnLwY4lMxLySJbLyHVOYHd3vdk2VatXQW4OXrvb2XplI+x9wl/eJ/TGbA7ewTAMw7jK/GtGqirDCnb3NKjtmZDZBVtx5wYYtxO1q+jb3WNitk3V3ecFw/7V7wTZi5HefSirl+wenjOxIkUZVpDdv27twWKlmGEYhsk4jLhzs2LvaeQ6O+7cvCLI9q8M4xRu2BQzDMMwjKfIbBOt2nvKrrPrzs0rgmz/yjBOwTPFDMMwjKe4ZXN5761jccu4SzVtD2Wu2bTsFpXr3nzvLADCmEv79x5zwp2bE+i1qZZpB0fxvIha+1m5T1vaw9j5lxacbg/j2spBqveAXVMbxjl0lWIiWqB1TAixynlxGIZhGDchon8CcFvs50AAuxF9F1wB4CUhxI/TKY+bEb/i83781b+q5q1lFymLmrW9/rSq3EbdzLmJXpvKTDuCbP/qFGrtJwDT9+naukbcsXofInERapLbO4imNpmMzHyCYv/+HsC1APoAmAxAU1lmvMGIz0zGO7h/GL8ghPi1EGK6EGI6gFcBvA0gJIS4BsClRJQScMkt3Iz4ZSdvWdQsvbztupmzi55sQTXtSCdq7fedNftw1xpz91JLexh3rdmfoBADie3N/eE/dGeKhRC/A6IzxkKIRUo6EW12WzA7+NX9iUwuq8fX1jXiztV1ABEgBH46b4KjsdV5accebs6EMYxViGgogDJEI/uujiVvRnQC5GjSuYsALAKA4cMTFT47uBnxy07esqhZsrz1ovC5jZ5sRzWUrWyKWCdDrf1ClBOdHoxDdi+daD2ffEkvSnv7xdSGuYhRm+KzRPQIgP0AqgD49jPGrwqITC6rx1vaw/i3P9TFzop+kf7rH+owpXKQIx8EvLRjj/hZB2WQveu5/Y71D8PY4BsAfg1gPgAl+sQ5AJXJJwohHgfwOBD1U2y2IC3bXDWPB52RHpw934WW9rAhW06tj3Y73hSmjRqkGipZiZplJO/KsmKUFObjROv5hLooaIV5ltXXTr0L80PJ2QBwzrSjpT2cYGdtZYzTm4SRTdA4MSGm1n4R0QOIRBVXdi8NK+kLrQdFaW8/mNq4STr6y2mMKsXzAHwOwEgAewF8zzWJbOBXBUQml53j699UjyG+/s0mfPHqy2zJrbW0s2DSCP6KNYibM2EMYxUiygFwnRDiHiKaA0B5uxfBYa9Eera5iseDu2If/Be6I4j09OAbT+81ZMup99FeWlSAedXDsGrnxePzqocZeu6UqFmvxplQxEfNSpZbkSt5BU9L7lnLt+Lt5g4AwDO1JzC6rBCvLJ4uvU5BVu/hl/TtzR8ALrukb6+t8ILJwxPaxCnTjrV1jbjz2X3oikRVwdwc4BGTq5Z69ZJN0Dg1IabVtwB0+1stn4fmjsNiFZtipb0VUxs3+sNr0tVfTmNUKY4AOA/gNIB3AQwC8J5bQlnFrwqITC47x/96ugNqaKWbgZd27MO+PxmnIaJPCCH+bDObqYhusAOAPYiaTOwCMB7AEZt596Jnm6somPGeHL62qhbhCNAW7gYAfGfNPgCEcHfqhEBrR6fuR3tLexira08kHF9dewL/NvNyQ++DJxdO0o2apeepQW8io+54a4LCCgBHmjuw6VATJgwvkU7syCYrao+1qOavtLkbph2K/ayiEANAd0+0/4xOSunVC4C0r52cENPqW7OeOZR89LxPeGlq4xaye9SvE5iA8RmBZwBMB3B77Jqn3BLIDn5VQGRy2Tl+Y1WZapla6WbI9KWddMC+PxkX+DoR7SSi7xLRMIt53AhAiYFcA+DLMRO5eQBedEBGAPq2ufGUFhVgQN985IcSl/dDlINQTuKytTIhoPfRDlycjFC71ijVI0txx6zRuhG9xlcMTHme9cpef6hZNa/1h5oNySyrt5E2rywrxtzqCscUsBOt51P6CYj2n9H21quXrM5vvncOOVC/T5xEq79l18wefym+MmWkZns73R9ek45n0y2MKsWDhRDfBtAuhNhh4rq04lcFRCaXnePKMl888ct8dvB6F3WmcMuEodixZAaeWng1diyZ4YslIia4CCH+EcA0AIcBbCGiTUR0g8k87hFC/DH29zlEJz12IWpScdYpWRUbXCPpWracybv3lQkBtyLWOYFe2bM0JixmVZUZkllWbzNt7hTDSvqm9BMQ7T+j7a1XL71ja+sa8bVVtfigK5JwzE5fr61rxJRlmzF/5W5MWbYZz9c1yi9ievHzsynDqPnEUSL6LaLuen6AqAsfX+JX5+Myuewcly3z2SETl3a8gH1/Mk5BRFcD+BKi7jGfjf17HMAGq3kKIVpx0QOFY4wcXGQ43awtp8w+1ojdr1volT1heInqNROGlxiSWWaHKrOHdgPFfvbbSTbFD80db7i9ZfVSO1ZSmI8lz+1HuDtRwSrIJct97eel/aAg60svn00ZJISxjcREdCuA0QDeAvCCMHqhhOrqalFbW+tEVgzDMGmHiPYIIarTWN5/A1gFYIsyDhPRDCFEWlxlmhmz9zWcwfyVu3tthAGguCAXTy28GuM1ZpPMRhLz8w53tbKNtIkd7xMKbk2U6JFu7xNqbdkvP4T/nD8R0y7/kKU6WLlnGXX8+mzqjdmGZoqJiBDdbNcC4BSAIfDhRjuGYZhMRwjxlfjfRPThdCnEZrGyTKq2qqK30lJZVqy7gqVcp9grpvPlqya3kTYxsrIkq3f1SHdnh9UoLSqwrIwq6NUr+ZhaW/YIgTGXDrBcvp+X9tXwo1szBSPPpt9kNrPR7jr4fKMdwzBMpkNETyYl+XY89sM+D7/Zhyqu4uIx6iqOScSN+8sP96xR/HZvZwJGbYoHCyHmEdFmIcSOmI9LhmEYJk0Q0XBEfcWPIaJpseRCAF3eSSXHy30efrQPtesqjknEjfvLr3uT4vHjvZ0JZNxGO4ZhmAxlJKJeIkpi/xOi/uP/0TuRjOHVMqmaj/ccImx5631c99EP2ZbJytK1ahjhHErxp2/XJlgvYl6m4cb95cel/Xj8Gpch6BhSioUQi2Ib7d6K/bvXVakYhmGYBIQQ/wfg/4joMiEEj8EGULMP/aAzgn+vOQAishVFy2pErmElfRM2cQFARziCg41nezdy6UUBNIJexDwmMwia7XNQMGQGETOXyEF0mU445XmCYRiGMUfMTzFjgNKiAnx/dlVKerhb4EJXD+56bj9a2sOm841fum4Ld5vKq+54q2r6D9YeREt7WDcKoBE2HWrSjJjHZA5Bsn0OEkbNJ/4AoBnAAQA3EdEXhRB/755YDMMwDGOfKy8dgML8EDo6IynHrC4321m61opoJ2L56kWkM2JGoRcxL9PNKLKNINg+Bw2jG+Y+JIT4phDicSHEPyHqko1hGIZJE0R0V+z//yKi38b/81o2GSu2HMWnf7YNK7YctXR9S3sY+xrOWJrVHVbSFxGNxc2unh50dUewprYB9c1thss1unRds7cBC3/3Omr2NvSmaUW0o1i+RiPSbTrUhCVr9qXMAOtFzNPCTvsy7qPXP1ZCTzPaGJ0p/oCI7gawB8AnAJwlomlCiG3uicbE44QvQj/7M7TK7Ee34s2mDowpL8S6b033WhyGcZPfxf7/oZdCmOWK772E891RpfTwyTY8uukoDv/4JsPXW7XdVYiPniV6BMIRgT550fmg6stKMHfFrt5zF0wejntvHSstt7SoAAUhwoU4vx8FIUoYVyfdvwFN5zoBABsPv49lL7+FnffcgJlV5RjSPx8nY8cUHrltQu/mLllEOj2b4ZlV5cgBEK+y58TS1bDbvoy7cP+kF6NK8W4ABQCuif1+A9Hdz75UioO661Yr+ovyUISI0BXpwQ8+MwZfmnSZoWuT87DyYLmpTMvk1mPE3S/2/n2wqQMj7n4R7/zk5t40v8rNMFYQQjTH/n/Xa1mMsmLL0V6FWOF8t8CKLUdx+3WjpNc75XYqfplZMaXo6o4kKMQAsGrncSyYNKI3fLBWuTV7G3D2QqI5xtkLEdTsbcCciRWo2dvQqxArnDzXiZq9DZh6+YfQej5xo11Bbg6mVF6cCX5y4SRN7xN6NsMzq8pRs7cBiXPYUQVZkS0eduvlb7h/0o9Rpfg+AMUAPgBwLYBaIUTqWpMPCOqu26U1B7BqV2Kc8HtvHZvwUCj8e81BgIAvXX2Z7rUKdh4sN79SZXLrMfvRrZrp67413bdyM4xVYn6KVRFCHNc65iU1+09qpscrxVofmU66nUp2sbWmtkH1vLqGMxhVVqxb7roD6pvW1h1owpyJFbrHRw5OzTs/lFonrYh0WjbDv3+9AROGl0hli4fdevmL5IkcI/0jm6DhCRxzGFWKVwN4HMBnAFwC4N8BXO+WUFaRfUH7lfrmtgQlC7g4Y9HRGUGIKOWaH71wCJ8aU47Wjk7Na5UHwOrA5+ZXql6djTy4bzZ1aKb7WW6GscGPYv9fgegkxT4AYwGEAVR7JZQec8YNweGTqfMnc8Zd3Jai95HpptupCTH3Z2rpJYX5uuXOHluOjYffT7l29thy6XG7dZpVVYZnkoJ/AMCO+tOYsmwzvnCV+se/Ils87NbLP6hN5EypHKTbP7IJGp7AMY/RjXaDhBDrAYwSQnwJgPSJIaIyInrDlnQm0dt162fqGs5opg8r6YuuSPJiGJAXijp717tWwerApyjTCeXGlGm7GJFbjzHlhZrpfpabYawihPgHIcQ/AGgDMDbmAWgCgLOeCqaD1oeikq71kalsenPT7VRlWTEWTE6cfF8weTgqy4ql5c6ZWIEh/fMTrh3SP793JlbvuN06zawqx+iy1PHvfFcPLnT14Nk9jfhQUZ6mbPGwWy9/oOXiD4Bm/8ieHdlxRh2jM8VtRFQDYA8R3YTooCzjYRhQnp1E6wtab9etH9CbsSgtKsAPPjMmajIRR6RHYFhJXxTmh6R5xm80if8KlQ18Xs3SGGHdt6Yn2BTHp7e0h30rN8M4QD8ANxPRAQBVsd++ROYeTO8jU1Gc3XQ7de+tY7Fg0gjV5WVZuTvvuQE1exuw7kATZo8tT1E69Y7brdMri6dj06Em/P71BuyoP43zXYmrgL/5f5/AsVNtmrLF4ze3Xm7sBfH7JnO91Vyt/pE9O0aeLSYVo0rxFwBUCSH2EtF4ALfpnUxEMwB0AEirt3DlC/pInAnF6LJCX5tOABdnLFbtTFzmUG7cL026DKCoyUReiBDpEb1KbWlRge61ClYGPqvKtBN1NsI7P7lZ1fuE3+VmGJt8CcBdAP4ZwDsA5nsqjQ6yiQqjH5luhtytLCvWfH5l5c6ZWKGrcOodt1unmVXlmDC8BFOWbU5IVyYAxlcM1JXNSVmcwo29IEHw3iCbgFLrH9mzwxM41iA7wemI6H+FEJ9NSssHsB7AHAA1QojpKtctArAIAIYPH37Vu+86u5k607xPKOh97bppTB9ULw5BlZsJFkS0RwjhS5teN6iurha1tbWGz79x+daUiYr4zc9L1x5I+chku0fjPF/XmDIB4Delzwgt7WFMWbY5YVN5n7wc7Fgyw5YbUqfzdAsr/Sh7dvjZUkdvzLarFG8RQlyXlLYUwGEhxLNEtFVNKY7H7ADLMIw6fl8izFRYKZYjm6jgj0x7ZMKzv6/hDOav3I228EV3dcUFuXhq4dUYb3F204083cRKP7L3CfPojdlGzSe0UNOorwcwg4i+AWACEa0UQiy0WQ7DMDoEYYmQsQcR3SWEeJCI/gsXx14CIIQQ/+ihaFJmVpXrrtrpmTAA9pQ+q0pBS3sYb753FgBhzKX9LSmbenLbzT+5Xn5Rhq2ueLqxh8VqnmbuNydXpq2YscieHdlxM2TCx5cMu0pxCkKIacrfsZliVogZxkXYwXvWEMiIdnax88Fn1SXV2rpG3PnsPnRFot8euTnAI/MmmPrQ1JPbbv5+dbUlk0sWJdDpvSBW8jRzvwU1LoIVsmXixa5SnOpANw6Z6QTDMPZhB/zZQRAj2tnFzgefVZ/iLe1h3LVmf6/CCgDdPcB31uwz/KGpJzcAW/n71Ve6TC4jfemGJwwzeZq534IaF8EK2TTxouunmIimaf0DgGR7YoZh0g874M8uiOhfiei3sX//RUS/9VomGTV7G7Dwd6+jZq96FDkt7Pgct+pT/ETreYRyUud7QmTc17me3Hbz96uvdJlcam3S2d2TUufWjk4cbW5Da0dimGwj1B5rwSPrj6D2WEtCemlRAcbHXJzqYeZ+MxMXoaU9jH0NZ9DSHpZVQZX65jasqW3wzMew1efQTr29qrNsplhRej8JoBtALaLO4osBTHVPLIZhjOKmCzrGl3wRwN8D6IH6vg5fMen+DWg6F1VwNh5+H8tefgs777nB0LV2PvisuqQaVtIXkZ7UZo0I4x+aw0r6oj1ucxcAtIe7e68Pd0Us5+9XV1syudT6skcAt/5yB975yc0A7JmFzF+5C9vro8rwY5vrMbWyFE8unGSqDmbuN6NxEeyaHfjBVMbKc+iF2ZMT6M4UCyF+JIT4UfRPMUsIcY8Q4iYAXWmRjmEYQ9wyYSh2LJmBpxZejR1LZmSkrRfTSzOAjQD+C1E74//2VBodavY29CrECifPdRqeMbYTcU0vYp2szIfmjkNe6OJsbm4O8NDc8YY/NFs7OlO+VkQsHQByVGaKl84eYyj/Mx+oz6BqpaeLksL8lBnwUA6hpDAa2a+0qACD+qnPw81+dKutCGy1x1p6FWKFV+tbUmaMZZi539QiCybHRdCKVGd05tQvUenMPod26u11nY3aFPcQ0b8C2A9gjIvyMAxjEb844GdcJw/RMM8feC2IjHUH1OM3rTvQZDiwhB07U72IdUbKtOodQs+UYFRZMfrkhtAVuTiTXFgQwpVDBxjKe9vR05rp1SNLDcvoNCdaz6NfXijB/Vm/vFDC3obGs+qK+5tNHbYisDnZJmbuNyWyoJb3Cbv7PfwUlc5Mu9ipt9d1NhPRbhGAeQDeBTDXNYkYhmEYPcoAvE5EvcaLQogZZjMhol8B+JMQ4gUiegLAFQBeEkL82ClBZ48tx8bD76umm8HOB5/MJVXtsRZsO3oa00YNSlCgSosKMO3yD+nmreWiSs+UoKQwP2UpOtIjepeiZW6vpo0ahMc216umO4UVN3ZGltjHlBfiYFNH8qUYU15o2iwkvp2cbhMz95ueu0G7+z38ZipjtF28MHtyCl3ziTjyATQA2I1o6ObZrknE2MZro3yGYVzl5wAeRNR0QvlnCiKaCqA8phB/DkBICHENgEuJaJRTgs6ZWIEh/fMT0ob0zzc8S+w281fuwtwVu/DY5nrMXbELX165y/C1a+saMWXZZsxfuRtTlm3G83WNvccqy4pVl9YVf8JaS9F6eSpUjyzF1MrE2c+plaWOzRIvrTmA65dvw51r9uP65duwdO0BQ9cZWWJf963pqteu+9Z0VJYVq9ZLTSlPbqf3zl7A5Srt7eXMOWDP/AewbgLkNV6YPTmFoYh2RLQXwP8AOKWkCSFMD8RqpDuindasgILsK732WAteOdSMDw8qxKwx0a9Do8t6sq/vp147hrX7T+LWcUMw/5qRpuqlLOG833YBW45cXEoyaqBuJ+pNzd4GrDvQhNljy33zsrOKzNl+EByXexnBKBujJ6U7oh0R/b/Yn30BfArAaTP+4IkoD8ABAC8B+D8AMwG8LIR4iYjmAigWQvyX1vVWxmw/jhG1x1owd0WqErzm9klSZUoWPri+uQ3XL9+Wct3GxdN6n4vk8cRsSGLZu8wKRuSWYWScnP3oVrzZ1IEx5YW9irLR+qudV5CbA0Ag3C10r/UKu++OoI6rXgTdMYITEe3OCSEedlAmT5DtTpXtloy/HgC++78HkZsD9M3Lle6ulO2mHP/Dl3H2QnRH8uvvtOKh9Uew74efMlSveAfiyRjxX2lnp6edneV+Q+ZsPwiOy73cteuHXdLZQNKExH/GzCDMsADAIURnm78J4BsAnogdOwegMvkCIlqEqAkdhg8fnnxYypyJFb5RhhXs2KHKbCaN2EUmL0WbtcOsHml9dlhNIT/Reh5vHP+bVG4ZRpbY1WaMjdZf7bxQDgGCAER0r/UKu/s9nIxKl07cNHtyC6PmE9uJ6PdE9Ol4P8VBQrY7VbZbUu16IOp0Xba7Urab8qnXjvUqxApnL0Tw1GvHpPVScyCejJ7/Sjs7Pe3uLPcTev1vdwdxuvBy167XO4aziSSf8bMBVJnM4mMAHhdCNAF4CsA2RGedAaAIKu8FIcTjQohqIUT14MGD7YjvG7TsTY3YocpsJq3YRabL33iy6cHStQd6f9//0mHVa9Jhz2m0/sNK+uJCd+L7sivSg4hgX+2MfYwqxV0A3gLwCUR9F093SyC30JsVAOTOqbWu1zo/HplT87X7T6oe10qPR8uBeDx6A5odR/B6O8uDhszZvtUAAunES6f+fg0okKF8EcBvEHXFdiWA1Savrwfw4djf1QBGALg29ns8gHfsChgE7NjmymwmrdhF2rU/NYLaB/6qncd7f3dGUsPUpsue00z9k80+hRBYOnuMq23HZAeGzCdivooDjWx3quwrVet6rfPjkc0a3DpuCF5/pzXl+K3jhuiWB2g7EFeQDWh2dno6tbPcD8j6PwgR47zctev1juEsYxKAlQCUh89sAI8nAPyWiP4OUfdu0wE8T0SXAvh0LP+s4MmFkyzb5spcVFlxB+dGmON41EwPkikqyMUDnxuLC12RtNuwGqn/idbz6JuXm+D6rW9eLq4cOgA7lswIxL4Pxr8YnSkOPLJZAdlXqtr1QNSpu+zLVDZrMP+akRjQJ5RwfECfkKHNdmoOxEeU9sXDc8dh4+JpUptOOzs9/b6z3Ax6/Z+OGRwn8HLXrtc7hrOMs0KIh4QQv4v9W2XmYiFEmxDiC0KIaUKIyUKIdxFVjHcBuE4IcdYNof1K9chS3DFrtGn73Jb2MJ7c+Q4e2XAEr76dOjkARJ+LudUVpp4DJSTxsVPtqiGL1eQwGkp3WElfdEa0FWIg+sE/+SOlpuU2KovMO5IsJLPeBIbRcM7pRK9N7IZ/ZpxH1/sEET0ihLiDiLYgOhuhrKwIK34x1WDvExdxwvuEmgNxI7D3iSjsfSK4ZXtFurxPxO3lmAXgIwBWAegAACFEqssAl0j3mO1H1tY14t/+UJeQNqR/vmObjJM3dWuFLDa7AXhtXSO+vboO3TGdMi9EmDTyErwaV5bVDbJGZHFqM+7zdY0pYe39uPE5EzZvZyJ6Y7Yhl2xuwgMswzBBJo1K8Q80DgkhxL1ul6+Q7WN2S3sYV//HRnSrvDofnTfO9sSAUVdxZl24qZ2fHwKIchDuNpaHFkZkccLdW3KZfp6k0GsTAKb6jnEW2y7ZiOhFAHuUf0IIbSNWhmEYxnGCvLcjk1YQTrSe1zTiNhO+WmvV0qirOLMu3NTOJ+QgJ2lnnRVXZmp558TSlXycDt9rxd1XOhVpvf4BgNykhvfKhVwmPZtOYNRP8VcRXbJbCOBZImoWQgR7nZxh0sSKLUdRs/8k5owbgtuvcyxYGMMEgkzzXz2spG+KhwYFo5uM9XzmGw1ZbNaFm9r54UhPvGtfaR5aqOX9QVcPnt79LsbHNtt6vRk33eYKev3z8sEmtIcjqsfSSaY9m05gdKNdHYDPA3gBwGgA5r23M0wWcsX3XsIDr7yNwyfb8MArb+OK773ktUgMkzYy0X91aVEB/mGK+p6P4j550utlPvNHDi5SvS453ewGYOX8glx1lb4wP2R5E3FpUQHuuP7ylPTVtSd6+9rLzbhe+JrX6h8AuO/FQynnf392VVpniTPx2XQCozPFYwHcCGAegIcBHEfUPybDMBqs2HIU55MMD893C6zYcpRnjJmswOklc79w7kKXavr6Q83Sjc4y84gTredRXJDocqy4IFd1aV3PhZmaqcAtE4ZiYL88fP2pvfig8+JMZWFBCD/6zBhc99EPWVbMLinMV02P72srbuqcwKypiVOo9c++hjMpshTmh3DlpQNck0MNs8+m3224ncKoUvwigL8C2AHgFwD2uiYRw2QINRrBV2r2n2SlmMkKvF4ydwst//Czqsqk19r1mZ+Mmm2tnqnAmEsHoCdpg32kR9hSiAHjfe1F+N50RQtUI7l/1GSJCJF20wkzz2Y2ecowZD4hhPiEEOLvhBDLhBDrhRDy8G4Mk+XM0Qi+opXOMJlGpvqvVvMPP7qs0JA7TLs+82XITAXc8rvu5772k695v8hitL+8MD3xEnbJxjAucsX3XkowoeibSzj845s8lIhxmnS5ZPMLVsbsTN3hbsc/vF2f+VrsaziD+St3p5hfPLXw6t5Nb3byl+HnvvaTCYBfZJH1l9H7KUjYdsnGMIw1Dv/4JvY+wWQ9XiyZp4OZVeWWgiUB0RljvSh6VlyOAcZNBazmn0yycufnvnaqzk6Qblm0lHBZf3lpeuIFrBQzjMvcft0oVoYZhkkLyvJ8csQ3NxSwbLI1DTJ2+imd95MfyEilWG85IJ1LFmbLki3FqYVTVso43XYB+06c1VyKky2R6B2XhZ+WLQMGHSv3jJ+XEPVwUm61drPz/OldK3t2gtofDGMFPa8UThFva6p4Urjruf2YUjkoYxWmIOJEP6XjfvILrinFRHQJgKsAvJHOjXl6zqjT+VVrtqxZy7fi7eYOAMAztScwuqwQryye3nt80v0b0HSuEwCw8fD7WPbyW7j7pioseW4/urp7EImZrSY7ggfkDrr1jo//4cs4eyHquuf1d1rx0Poj2PfDT/Weq+eEPhOwcs8E1SG6k3KrtZsALD9/ev0ge3aC2h+ZhB3bWz20PpTSNfkh+9jSk0M2maA2CaKWd2F+CB2dkZQy7CzPG/mI9MrNmRG02t0PH8dO35uy/JzqJ79HEHQKV5RiIhoC4I8A1gF4hIhmCCFOuVFWPFrOqBdMGoGSwvy0fdWa/TLbdKip96WucKS5A5sONWFmVTlq9jb0KsQKJ8914s7VdehKNPUBcNERfPXIUt02qSwr1j2+6y+nexVihbMXInjqtWOYf81IXSf0mTBjbOULW9befsVJudXa7Ttr9gEghLvNP396/VB3vFX32Qlqf2QSso8Wq2h9KKVr8kP2saUnh2wyQW0SZOc9N6TkDQAXunpQECJQDjlSV6MfkX61NdVqdz98HDt9bxrJz6t+CqppjdGIdmYZA2CxEOI/ALwCYKJL5SSg54xa+VqKJz4OuZOYLWv9oWbd9HUHmlSP9+g4DlEcxOu1iez4Wg0/u0q6nhP6TMDKPSNrb7/ipNxq7RaiHIRyEiNpGX3+9PpB9uwEtT8yBb0PfjtouYmqb25Li/soWTQwPTdWsoh2WpMgNXsbUvK+EJsVCUeEI3U1E+XML67F4tFq99pjLZ5Hb3PatZnR/LzopyC7cXNFKRZCbBRC7CKiaQA+AWCnG+Uko+eM2szXUn1zG9bUNlh+YMx+mWk5fFfSZ49VX27MUY/WCeCiI3iZg26947dq+NNV0pUytMoOOla+sIMarMBJudWd0/cgkvQVZ3S2Qq8fZM9OUPsjU5B9tFhF60OpLhYtLDnd6ckP2ceW3oecbDJBaxJESVfLO7kMq5j9iLxlwlDsWDIDTy28GjuWzPB8JlCr3bXaPJ0fx05PzJnJL939lM5JSKdxa6YYREQAbgPQBSAiOd0R9JxRG/1aWlpzANcv34Y71+zH9cu3YenaA6blMPtlJnMEP2diBYb0TwyhOaR/Pn46bwL65OUglKQcxzuClzno1js+/5qRGNAnlHBsQJ9Q72Y7mRP6oGPlC9vPDuz1cFJutXZ7aO54PDTX2myFXj/Inp2g9kemIPtoUWhpD2NfwxnDM0lqH0rh7m5MqBiYlqVi2ceW3oecbDJBaxJESVfLO7kMq1j5iCwtKsD4ioG+sBnVanetNjf7cWz2PjUim9X+shL50O1+UtqnMD/kS9MaI7gevIOI7gNwUAjxTFzaIgCLAGD48OFXvfvuu46WadX7RH1zG65fvi0lv42Lp1l6iTrpfaK+uQ1PbP8L3mk5j9uqh7H3iTTC3ieskS3eJzh4hz6T79+Ak3HmAEP656vax5q1PXy+rhH/9oc6xL/BFkwejurLLklxH+WKTfHaA1i1U9tG9fm6Rk05vrxyF16NM6FItimWtZmSN+CCTbGkXn5Hq93t1ssJG1m9e8IKTudnh+T2mVc9DKtrT/hCtmT0xmxXlGIiWgLgpBBiFRH9HMA6IcQrauf6KaLdmtoG3Llmf0r6w3PHYW516u7fdOGHDQIMw6jDSrE2Le1hTFm2udf2FQD65OVgx5IZKC0qkB7XQ28So6QwP+u9T7hZL7/jtPcJO/epUdms4gcPD1rts+5frnX83nQCLyLaPQ5gNREtBHAQwHqXynEUP9ofGtk974eHgmEYJhmZOyg77qL07F/nVldYHgvNjKeyaGB6bqxkEe3mTKxIWRFUk6mkMB+VZfbG/aBEpTPSN3rnGK1Xch5Oup9zOpKdH6L0abVPR2ckcKGgXVGKhRCtAG6QnugzFPvD5CUWLwcHvYG/sqw4sG5PGIbJfGR2j3bsLN2YxPDjeOq0z28j+XtdZzWMyOlEXdTymFI5KLA2sunAr+75rODaRrugcu+tY7Fx8TQ8PHccNi6e5rmZgt7AH2S3JwzDZD6yzap23EU5vYnSj+OpmkzfWbMfd63Z54icfqyzGkbkdKIuWnkA8J37OT/hR/d8VsnIMM928dPSkd7s9b6Y+yE/RhRiGIYB5CFi7YSQvffWsVgwaYQj9q9+jNCmJlMohwBBiHfqZFVOP9ZZDSNyOlEXvTyyKdSxFTKlfVgpDgBaA38mLVkwDJO5yOweWzs6cbS5DYX5IdMvU6cmMeyOp27s7VCTqTvSA0pyw6kmp5FNZVp1LswPYV/DGVN1cXNvi5G+GVbSF52RRO+vnZEIzp7vREt7uHdjp56MsnL8YL+bjJ/2FPmxfczCSnFAUBv4lSWLZJcsQb8pGYbJHvziXcfOeOqWXa4i0x2r69Ad09UEgL/7eEWKu6t4OY22qVqd5101DLN/sd1UXdy2SzbSN9vrTydEeSVEo75+4+k3euu1eo++i7CgvVODYg8eJFz3UyzDTy7ZgoqfvhQZJttgl2zWcdo3vBOYHU+ddNellf81P9mMcLcxd1dW2jTevdvsX2w3VRe3668mZ3Kd1WSQoSdjEN6p6Wz3TENvzOaNdhmAnyIKMQzjX4gol4iOE9HW2L+xRPQjInqdiH6RbnnMhhVOB2bHU6shbeub27CmtgH1zW3S/PNDqfnHu7uKj7JmpU2VOnd0RkzXRa3+ISJXQvpq9Y1e6Gst9OoVhHdqkEMp+5lAmk/InJrrOUWXfQHaifwmi6olK9uOM3dZ2bK87TiZlx1f/sphrN3fhFvHlWPxjVekHLeDV1/0dqP4ORXZ7dW339d9FtyQ3SqyOttpE1mdgh6MwEHGAfi9EGIJABBRNYBrAXwCwBIiul4IsTFdwvjRN7xZrNgimzEZ0ctfbfncTptaqYvaNR2dERx872zafNTqhb7WIuj7b3hPkTsEznxi0v0b0KQT/nL+yl3YrhE+U2Z/Ixuoko/ffGU57p1zJUqLCjD9oc14p+XiF9roskK8snh6729Z2Xpyy+o9a/lWvN3coVn2LT/fhv2NF2cjkvPWk00ml+z4qO++iK64WyyPgKMP3Awn8MqeSlZnGXbkfnrXu/jRukPIDxHaw4mbSpKfBSuyu/WRIauznTaR1cltm9UgmU8Q0T8D+BaA9wG8C2AfgHYhxK9iCvJnhBA/0MvDaZO3oIcVBsyF27Vi3qCW/5TKQZrL5z/b9LblNrUSOvjpXe/i32sOJqSleyk/We7kMMN+DjtsFT+FeQ4SXkS0c4WavQ0JiiEAnDzXiZq9DZgzsQK1x1oSXo4A8Gp9C2qPtWDk4KJe/4OKu5W7ntuPKZWDUFpUII0cp3b8xYNN2HC4CR8ZXJigEAPAkeYObDrUhJlV5Qm+D9XK1pO7emSpbr2L++QlKMTJZX/m59twoDFxeS4+bz3Zjp1q15VLJvfyVw4nKMQA0CWiM8d2Z4xlbeoWsjrLsCN3/Iunszv1ePyzYEV2tz4yZHW20yayOhmJCJllvA7gk0KIk0T0SwB9ARyJHTsHoEztIiJaBGARAAwfPlztFMs46VbNK8y4o5IFZDKav55LTjttasW11pVDB6CoIJTwoZ5u125qcv/bzMt1fwedTHGD5icCZVO87kCTbvq2o6dVj287elpqfyOzw9I63hkBDjd1qB5bf6gZgNz2R09uQL/eL+w/qXrshf0nUXusJUUhTs5bTzaZXLLja/ery62Vbgav7KlkdZZhVe6W9jB+9MKb0vy17hU9GbcdPe2qE39Zne30paw//Giz6jH7hRDKoPEWgHZEFWMAKILGO0EI8bgQoloIUT148GDHhaosK8bc6gqp8tbSHsa2t9/HtrdPeR5goqU9nGDLa9QO1ah5Q7LNcXL+URdk2svnRttUDbM2tcNK+qK7J3EGxOpSfnK7mrkGQILcyfUwU69kOazIlQ6CYP8cJAKlFM8em2orG58+bdQg1ePTRg2S2t/IBiorNm6zqqKTLrKy9eQG9Os95tL+qsfGXNpfV1FT8taTTSaX7Pit49Tl1ko3g1f2VLI6y7Aq94nW88gLyR9XrXtFT8Zpowa5+pHhZqhfWX9kgs2qwzxJROOJKATgswAKEbUpBoDxAN7xSjAZa+saMemBTVjw29ex4Ld/xtX3b8TzdY2eyTJl2WbMX7kbU5ZtNiWHkUh8S2sO4Prl23Dnmv24fvk2LF17ICWf7fWn0dmd+NzMqx7miXLkVEQzK+1qpy+M5rm05oDjZTD+JFBK8ZyJFRjSPz8hbUj//N7l4uqRpZhambiEPbWyFNUjS6UPrWygUjuux4jSvr0b3mRl68ktq/fnJg5TLf9zE4dpKgzjhhb35q0nm0wu2fHFN16BvCQn83kERzbbeRVWUlZnGVblHlbSFxGJ/X/8s2BWdjc/MtwM9SvrD6dDAWcA9wJ4EkAdgJ0AfgzgY0T0MwB3A/i9d6Jp09Iexl1r9qMrcvEZ6O4BvrNmX9pn7pxYVbn31rHYuHgaHp47DhsXT0uw99Uy+Yn3UhFtj31IHhGeef2EZzOZt0wYih1LZuCphVdjx5IZpk2vrLSrGytcanmu2nXc96GwGWcI3EY7wHvvE/+14x08u+c4CnJz0dXTg49fVoJX4+warxs9CP/1D1enXOuW94nn6xrx7dV16EH0K+en8yb0DkhfXrkrQbaxQ4vxwjenmZKNvU+k4oX3CWVTRYgIXZEe/OAzY1CYn+OY9wm3N21kqveJIG20U4OI+gK4GcBeIcRfZedbGbNXbDmKmv0nMWfcENx+3Sjdc9Xug30NZ/DF3+zCB52Jm0v75YXw+0WTbHk5MHtv7Gs4g/krd6MtfNGwv7ggF08tvNqUHFr37JraBty5Zn/K+Q/PHYe51RW9Mnzx8V34oCupPfJD+P3X5O1hx8uSW1hpV6f6ArhY5z55IXz3jwcS8kzGahmMP9AbswOpFPuB5IHba5dPdpRaLbxy3cVo4/ZHQBCc1vuNoCvFZjE7Zl/xvZdwvvvie6ZvLuHwj29SPVdrs2dLexjVP96YMjNakEt47e6Zlu9VK55JnAiaoOcxxYh3imhAj00Idye2SF6IsOu7+u1h1stSuryBtLSHcfX9GxFvEZKbA+y+53rXA4ck15mAlHstHg6SEWw4eIcLJBu329nU4IY88VSPLMUds0abUmznr9yFuSt24bHN9Zi7Yhe+vHKXk+JmBF5svHB7UwVv2mCcZMWWowkKMQCc7xZYseVoyrl6S+GtHZ2qSsq3bxht+V41Yqaghl3TLT2PKYAxk5/SogIsnT0mJW/ZJJeszlbbxCmISPd3Mk6Y0anVWQDID6E3zwWTh6fdVI/xhkC5ZGPSg123Y0HE7Ew/x5xnGDk1Gt5xavafTDGjUDZ7qrkYO6qhlF1SmK+abgQjrtG0Vk7suMLS85iijK9GXKqpuUHrm5er6wZNq87b60+jozOCN463al7n9oTPidbz6JMbQlfkotlCn9yQ1K2bXbdkWm1yz01X4GPDL8lYd25GybbVQ1aKmRSMDNqZhNnlQq98JDNM0JgzbggOn0xVaOeMG5KSprfZszA/pJq/HS8iMs8ksg/f0qICS8/7tFGD8NjmetX0eCrLinUVUStu0LTq/MCf3kJ+KCfFxZvsOiexs9nXal8A2nW7tnJwyux8to3v2Tj5w+YTTAp23Y4FCSvLhRxznmGMcft1o9A3N3EJvG8uqW6201sKd8OLiF6ebvrtdgorpgNqdQ7lEMLd0XqGu3sQyknsr3R5a/HKoxB7qFEnCM+AG/BMMZOC4ubq1aSNIJk4S2wluhTHnGcY4xz+8U2GvU/oLYW7EflOK089Uw67SpqTK3FWTAfi66zmaaFfXggPfG4sLnRF0r5x3KsIbZkQVdFp3HwG/AwrxYwqTy6clBXeJ6wEd1BmNJLdl2XyQMEwdrj9ulFSV2wKesvUMpMCK6jlKfvwtWNnadR8YtOhJqw/1IxZVWW9Pu/jid8HYdY1WElhPkaVFaMwP6Raz8kfKfVsPNPqf7dtW+3eW155oIpvFwCOtVG2Tv6wUsxoUj0yM2eH41GWzlbtTLQplg1qHHOeYTIXvQ9fu3aWRlbiZi3firebOwAAz9SewOiyQryyeHrvcTtu05Lln1c9DKtrT/j6A9/vtq1eubGLb5fzXd0gouhmRQfaKFsnf9hPMcPAu698Jviwn+LMJXl20im/uIC2H/hNh5rw1VV7Us5/YsFVmFlVbsiPsV591ORf9y/XoqMz4ssPfCfb3A3s9Icd1NolHqfaKBO9T7CfYoaR4LWfaYbJZGr2NmDh715Hzd4Gr0VJ8S+u52882W+32ibbHBDefO+caln1zW1YU9ugunFXy3/8+kPNqnkp6Xr7IGRla20S7uiMJNTTjg922bVm8/b7xmaj/eE0au0Sj1Nt5Fff9W7FCQik+cT3/rgPf3qzGZ8eU4Yff258ynG9Lxu7M4J6ectswGTIrtez8ZVde+cze7Hh8Pu44YoP4eHbJprKO77NAKS0n53wvWbs5tIdjlRPNjt9pRx/+c1mVA4uxA1jynvbZdOhJjz953fRLy8XX7lmhKWw2nbaxMsZc1nZeuHdZW0i6y+7zy6jzaT7N6DpXCcAYOPh97Hs5bew854bPJHFiOmA3pKzmp3lB10RfG1VLR6am3it1SX1WVVleKb2hGo6oL3f4W8fdErLNmInasdUQXatlbz9bttqZV+KE6i1Szx+aiOncdOcJnDmEyPufjEl7Z2f3Nz7t15j2bX70cs73gYMQIoNmAzZ9XphQWXXytpML+/kNotnweThuOqySywPgjK5vQxHqiebnb5SO55DwKO3TcAvthxNyNfItcnH7bSJV3ZxRsqOV6wAYEj//F7FStYmsv6y++yy+YQ2NXsb8K3V+1PSH503LuXDxm1ky82AsSXn5+sa8Z01+1JCLMdfa3dJ/cblW3FE5568a80+rE5SnJXyWzs6dct+vq4xxU5UGZPtmCrIrrWTt57MfmDp2gMp+1LSMXbGt4vTNsV+xQlzGr0xO1Azxd/74z7V9Duf2YuHb5uoG1ShtaNT1R/tJ0aU9u601Zup0su77nhrijJzpLkDmw41GZp1qtnboHr9b7b9BZ+bOAzHTrVrRpg7e75Lt+xvPvW6apnffOp1LPzkKJxuu6CZ98B++ZoKMRBtvz/8+QQ6I4ltUpgfwr4TZzF+2ADTbVaztwEjBxejqzui2l8LJo1AZVmxpn9h5TigP0Mtm73WalMAuu2tFQ1w06EmDCruo9rePQK4Y/W+FEf8yrVKJEFZpEEjbaJVd6PXytrODC3tYbz53jm81/qBbtk1exsSFGIAOHmuEzV7GzCspJ9um+j15cyqculxxh7rDjRppqdbKVZzMZWMEZdTt0wYioH98vD1p/big86I6rVWXD3G88ri6bqrF1+6+jK8uP89dHQaj/6nlK23SdiOGy7ZtXby9vvGZq9cuiW3C+Cc9wm/4rarOFeUYiIaAOAPsfzbAdwmhOjUv0rOn95Ut7Va88ZJTBvdiMtKC02HCf326n2gHKD6spKEl2vyl55eR+jZgMlerGvrGnHHs6kzKQCw7OW38NMNb2P6qMGqx7cdPY0/H2tRPbb+UDPaOyN44eD7qsdfOPg+Nh05hfNd6isFK189hutjy3V6JEdA6uruSdgkkuQHXtpmd6zej8KCXJzv6lY9rgzsspeO3gy1bOlFZtOndWxmVbmmD9JFT+5Bv/xcfNCpXq8enRUbxX+pzL+pkRexVt2NvsSdWrZaW9eIb6+uQ7e2ftJbtp5iVTWkv+oxpU1kz6adZ5eRM3tsOTYeTh2DZo91pm3NfKDJlpsB40vOYy4dkPLMxl/rxJL6zKpyzXtwWElfRJKGDDPR/5LdnintqOWizUibyMwc7JpB+D2inBvuAo2Q3C5+biMjyJ5pt81p3Npo9yUAjwghbgDQBOBTTmR6zcgSzWPKDKVWY2kNRuFIDy509aTMNiVHNdPriFkayqNWuoIy+6wyQQgA6O4BLnT1YNNb6i/u8cMG4PV31GPVlxfnY8lz6sq2wgddAlqq2Ka3mjGitJ/u9WokD9TJdZO1WQ+AtnC3prKk9KPeS0cvEo+RKD16/Snra62ofxERrVdy+yjkEKkfiMtTFmlQ9iLWq7uRl7hTEY5a2sO4a80+XYU4vmwtBWr22HJpm8j6y+qzyxhjzsQKDOmfn5A2pH++I7PEa+saMWXZZsxfuRtTlm3G83WNuueXFhVg3lXDEtKmVpZaiqAmi77mdpQ0J6P/xbfj7F9sx7zqYa60iVcR65jgYOSZdvs+cmWmWAjxq7ifgwGoT1ea5JMfLdec+VR20Gr51SstKkjxRysjfpZMz2ffzKpyjC4rTLEBk800GVnOA4A+ebkYM7gQdSfO9qZNrSzFoOI+COUQIiqaVlNbp6G89crMyw0ZbrN++SF0dveomgCEcoB+ebnSNjODnn/hfQ1nNGf1lb/1ll5k/al3TM0HKQEJHx/Jv3MIeGTeePxyy9GU9oj3Xyrzbyrzuay32jG+YqDUX7NTy1YnWs8jRDkAIprnxJc9Z2IFlr38Fk4m2RQripVem8j60uqzyxhn5z036G6StIKeOZvWvdjSHsbqPYl2uK+/22rZHZlsSd/tJXUnov+ptePq2hOutYnfzSAY7zDzTLt5H7lqU0xEkwGUCCF2OZGf3tKTMgM5vmKgdKDYXn8K9790GJ3a72TV8vQ6QmYDpoba7HMOokpS/B6Orp4ePPGVj+PYqfaEHfbRGbpkFSvKrKoyrN33Xmr6Rwdh09unEZHoykp7Jg+uZz7oxBdX7kZXnCJekEv4z/kTU0wnFB6ffxUGFffRbbPJH74Ed//vQd1NMEDih4rWwC9bXjGy9KLXn7K+jo8GOH7YAHzj928k1KsgLwe//OLHsPOvf0vwPnHLhKFS7xOySIN6L0NZu8hepE4tW0WXflP7OT+Ug3tu+iiurRyUUraeYiVrE1l/WXl2GXPMmVhhWxmOX1a18oGmdY3ijswKsiV9JXJcSWG+5jl2sBr9T2nLs+e7LLWJ3hK3rE3SYQbhxL6HTPTP62fMPtNu3UeueZ8goksArAfweSHEu0nHFgFYBADDhw+/6t1331XJQZ3kXZ55IUIoh0zbNibvZv34ZSUJs01e7B5VZlIBGN5p+3xdI771TF2CmYKy+15rx+7yDUfws02pYUbzcqIzxEbK1JLvyyt3pczaxXsCMNoW4e5u1Y8W2e5tZSA7+N5Z3LfukKqM6d7J7Ked03Zlcaouz9c14o44m+K8EOGnXxgfyN3S7H3CfZJt2b9/cxXue/GQqR3o6Q4C4dcobPFydUZ6EOnpSTBlkrWJX+ul4IR8fq9jJpLO51NvzHZFKSaifAAvAVgmhNigd66VAVbxEjGitB/yckOWv+SSvwT9ELs83nm60a/UlvYwNrzZhPpTHfjUmLKEmTKtfP5uxWvYdeyiPfLUylI8+ncfM1Wm1rkyn7FG8/3ZprdNublRe3FeOXSAae8TbuCnWQe7sjjtfQIQGHPpAM/bxSqsFLuL1svy+7OrND98tUjXB6pfo7CpyZUXIuQQkB+Su/Lya70UnJDP73XMZNL1fHrhku2rAK4C8O9E9O8Afi2EeMapzJ3a5Zk8/e6X3aNaaXrX/93VlxnOGwD+cPs1qsqrmTK1zq0eWWpaGVbL14xNnpo90n0vHtIcyNK9k9lPO6ftyuJUXUqLCjDtcnXPKgyjoLWseuWlA7BjyQxTH2jpsml1222Uk3L1yQ3hl1/6GAb0zZe2iV/rpeCEfH6vYybjB5tztzba/RrAr93Im3EOO8prujD6ocIDGcP4FzurC2q27J2Ri5vAzOaXjg9UP0Vhi18B1ZKrX14IR5vbUJgf0m0bJ+rl5qqZTD4jvuvtuKXTwuuVQrMrz17K6vUEUqCCdzCMFn56CTEMcxG79pmKK7X4AC/dPcCO+tO+tfPU81aUTtQiRSbL9fHLSjB3xa6Ec7TM1OzWy21bXT35zPiuVwv/bbXvvLZPNlO+17L6gcCFeWYYLfy0oY3JHtimWBunbDyv+cmmlLDKBbk5eO1uf9t5ejnrphdquqQwHydaz6OrO5KgEMefY2RDs5l6pdNWN1k+vbIBqB6z6pYuWQ4v7ZPNlO+1rOkkY8I8M4wefrBHYhjmIk7ZeKr5tQ7lkO/No7xcCt5erx4Bs67hDK776IcAAAffO6d5rZ5CGO8e1I+2usntrle28reTrvqMlJuO+8JM+emS1WvzDBmsFDMZhdf2SG5jx7MHw6SbYSV9U0K2n+/qNmXWpOXXOtIj2DxKg7V1jXjgT2+pHmvp6MSUZZt7XbKp8cCf3kJ+SH3Fbf7KXb0RYB/bXG/Y7aaXJm5O+K53o1y3MVN+OmQNgnmGW2GeGQ+ob27DmtqGhPDUTOYwf+UuzF2xC49trsfcFbvw5ZWOxMRhGFehpBDmyb9llBYV4KG545Eb97bKCxEemsshgtVQPPGEVeKoz6seiuUb3+4N1R7u7kEoJ7E/QjmEcLd6KPfaYy29CrHCq/UtqD2WmKaGYu/rRZhnvbLdlMvLOpst321Z4z1Eqd1bfoFnijMEtQ0V6Qg+wqQHvZcRzxgzfuVE63n0yQ2hK3JxtrhPbsj0kqxiGuWEX2u/L9/aRW0ZvCA3Bz/9wnhUXNIPfzrQnHCsX14ID3xuLC50RdAnL4Tv/vEA2sIX+yt+CX3bUXWTjG1HTxsah7w0cdMr+5YJQ1E1pL8rcQrSVWet+9pM+UbOtfr8eG1KYhRWijOA+ua2BIUYAFbtPI4Fk0Z44ndZjyCE0/XjS9Puy4hhkiGiMgAvCyE+RkRPALgCwEtCiB87VYaTS7JO+LUOwvKtXdTanAiY/JHoONEeTjRnaQ93Y/JHSns3pOn117RRg/DY5tRoqNNGDTIsn5cmblplp8Mrhpt1lslvNu6B1rl22slrUxKjsPlEBlDXcMZUulfMWr4VX121B8/UnsBXV+3Bjcu3ei1SCmvrGjFl2WbMX7kbU5ZtxvN1jV6LBED7pWPmZcQwSTwMoC8RfQ5ASAhxDYBLiWiUUwV4vXwcT1CWb+2i1+atHZ1I9jclALR2dEqvBYCRg4tUy9RKDwJBvy/SJb/dcvw0FugRyJni5Jk8K7OP9c1t2F5/GoOKCnq/ko2Wp3asMD9k2X2L3ZnJCRo7ZLXSzcphNl2tPzYdasLbzR0J5R1p7sCmQ02qfebFbK1aVLy7ntuPqiH9VftWze2Plsx2Q4hXjyzF1MpSvBpnQjFh2ADdl5Ffwjl7SSbUwQ2IaAaADgBNAKYDWB07tBnAtQCOJp2/CMAiABg+fLipsvziFSYoy7dOoNXmehMoyrik118nWs+juCA3wbyiuCA30G0Y9PsiXfI7UY5fxgI9AqcUJ0/fD+ybi6Zz0a/cZ2pPYHRZIV5ZPF03j2T721AOYfm88arLAEYcfosegXBEoE9edOLdzJKCE8s2lWXFWDB5OFbtTLQpNqN8acmhlf69/z2Ap3cfR34OQKGc3vRZy7f2Kr/x/bH+ULNquesPNacoxV4tcao99KJH4Kafb0dBSL9d1Jy9KzI7Ze/95MJJqD3Wgt+8egxbjpzCX051YMqyzartY7cNM2GZORPq4AZElA9gKYA5AGoAFAJQlkTOAahMvkYI8TiAx4Gon2KzZfrBK0xQlm+dQq3NjU6gaPWX3yPaycpQSzdaJ79+YKfrvnaqHD+MBXoESilWm8lr6upMOOdIcwdWbDmKjq4eVbdVava3kR6B76zZjymVg9Da0dk7o1dSmJ9S3p3P7sPfOjpx5aX9e48pKH/f+ew+TKlM9d+YPFuoVp9v/aEOPT09mDOxIqX+erONCyaNQFlxHwACN44ZknK8Zm8D1h1owuyx5Sl5t7SH8e1n6tAtkDJDqjZzeu8Lb+J0RxcAINwDoKcHd6yuw8nWD1Rng5e/chiNZ86n1AcAZlWVpciS0ibPaLeJndnZ5ONqD304IgAIdHYntktyeykfJPHtpNxPTtp7jxxchC1H3kdnRPS6U1LKUuqvNeOtdk+qoXd9/PNhVn7Zio6sv5a/chhr9zfh1nHlWHzjFZbroNYGdmfyA8bdAH4phDgT8wTRDkB5sxUhQ83q/BJlzkvsTqD4PaKdXhla6Ubq5OcP7HTd19ny/AQqot2+hjP47K92oMeEyMk+FNfUNuDONftTzivIzcGMj34IfzrY1Jt285Xl2Hb0dMJSUTy5OdFwo2rcfGU5fjn/qt7farOFn59Ygfkrd6vmnzzjrTfbKJuJnHT/ht7ZdAAY0j8fO++5off3tGWbcbw1UWktLsjFDz5ThR+9cChBvoKcmCLsAGqz+vsazhhuE72BStYmWseVqHjxHzvxFBfkol9+DprbOlWPx5/31MKrcbS5TfV+e3juOMytTlXyZUx7cBOO/+2CalmKo3m1Nkw+Rw+t668dNSjh+TAz4x2/ggCYu78BYNR3X0RX3HOfR8DRB242XQe1NrA7kx+0iHZEtA3oXQ6ZAOBvAH4lhHiYiH4E4IgQ4n+0rg96FFK/zvilE7sfgX6NaKdVxrp/uRazf7Fdt2y92eUgRHpL132dCc+P3pgdqBmB020XTCnEQKoPRa3lo54ekfDCB4AXDzYh3B1RPR/QVoiVaxV/wVreIbq6IykzkwqKva3e9fXNbbrHgOgMcbxCDAAnz3Xi0Q1H0NIexqZDTSkKMQCc7+zGhIqBKfJ1OqAQX/uRUjyx4CpVMxe12VqF+DbRM/rXa5NNh5rw9SdrNY/fMmEovvfpj2rKfr6zW6oQAxeXlZyy9waiM63JCjEAhLsjCUtYdpe5VGfMuyMpz0f8fSaTW8ueHNC/v4HoDHFX0nPfJaLpZuqg1gaysjMRIcQ0IcR0IcR0AHWIKsZfJqJHAMwD8KLTZfrJh3ppUQHGVwwM7AvdCc580Injf/sAZz6Qj2Vq6LVhS3sY+xrOpGzAUszT4omPKie73ghaZdQ1nDFUtpk8jVybTtJ1X8eXY6ev/EqglOJ9J85aui7enZWyfBRPKIcwT2PW7gtXVaBPXg4KctWbKjdECGm0orKpQWtzwzstH+DBz4/TlFuxw9XbHCHzPLHuQJPq8Z9tqsc1P9mM37z6V9XjHx9xCSrLilN2i37panObbNQYWtJXc0OkskSTo+HfX2kTvYFKq02+9MQufHXVHrz8prp9s3LdVg33Z4Rou6jxkcGFve1UkEv4xvSoWaba/WbW3ltByy574vDEgdDuLl+1679wlfrzYcTDiZ49uV4eSvra/er3sFY6YLwNguK5xS1iyvE5RDfb7QJwnRDC2kCrwdKaA7h++TbcuWY/rl++DUvXHnAye8YkbgYB0vPeY+RD1a73H60yJlQMVHVFp5Qtk7ujM/Hajk5zURkzEb96arJLoGyKtXwkfvfGy/HXlg/w4dJ+eOCVt1Wvi+feW8diwaQRCd4nWjs68fSfj6dc+w9TRuCOWZdj519O419+X5dy/A8Lr0bTuQuqx5TZQKuzhYq9rZXrlWOzx5Zj4+H3U44LAOHuHvz5nVbV6xdOHQlAfbfopsNNOHlOe4bhx7dU4cB751BenI+fbUlVupPtiJO5ZcJQ9PT04FurU80OlGv1BtjC/JBqvs06MgPyNrvvlioMGdgXO4/9LeXYPZ/+KCYML8HTu4/jl1vq8fi2v+KXW+vx4OfH9d5vdm1WZ1WV4ZnaEynpX5v64ZQ0u7t8k6/Xej6MzHhryW30/r51XLnqfXTrOH1PM0bawMmZ/CAjhGjFRQ8UjhEkH+rZgJtBgGR2/DKbVLt7IQBtu1cAuq7o9Mpt7ehMWaHuEdFrs3W1wYm+8iuBmilW3FLFM7WyFLdfNwrL5o7H7deNUj2u9rBXlhXjK1NGYvb4S1FaVKA7o1daVIDZ44eqHq8eWap5TBn09fLWmpEqK87vnU3Vu142EzlnYgWG9M9XLQOIPtxDBiTexKPLChNmcpOXZXbecwMenTcO11/xIVz7kcSZ0wWTh2P+NSOxbO54LL7xCowuK9TNW4s5Eyt0r9WbCVRrk8rB/XTLk7XZkP75mH/NSMysKteV61db61VDpFaWFWNudYUtRUBWdjJ2l9Pir7cz4y2TW5b34huvQF7SykEeQbrZLrkOajg5k8+kku0z8X5DLwiQXYyYGdwyYSh2LJmBpxZejR1LZiRsVnPKTEGtDL37UFYu38OpBMWkxAqB2minUHusBduOnlb1LmHkuB5mPRbYvba+uQ3XL9+Wcu7GxdNM5S8ru2ZvA3619S94+/2OlGM//EwVKkr6Wo40JyvbThQ72bVGvU+829KBr67ak3L9P15zGf7+6ss020zLY4eaXHY3uBnFy6iAdjbopNP7hFns1CtoG+3sYmbMNjO+ZRvp3rDU0h7GhjebcPf/Hkw5tub2SY7MFNvZkObmhja9+7CkMF+3XL6HUwnK5kMt9MbsQCrFZgiCq6Wlaw+kuMix4sdWhpMPdxB3oN64fCuO6HhAsEvQBwrGGqwU65Ou8S1IpNvFV3x5HZ3dCeYAyR6a7KB477FaL7vX66F3H8rK5Xs4FTf7ym2yVil2KmhCOkiX8u7Ew+1nn40y3J5lDfJAwViDlWI5QZicSBfp/nhWKy8/lIMFky/Dp8aU2Z4hVivPr5E09e5DWbl8D6cSxMkxQH/MDtRGOzMEbYOHYh/sNnY3fXllYO/UgDSzqtxVk4MghLFkGMY70h1WWK28gtwcfGb8pY6adSnYjVjmZsQzvfesrNx0vaODhN+j01khY5ViIzHesxU7D7cXceKDNOMPZOZAwTBWCdrz6zbpDjedbeGtGcYOgfI+YQZ2teQO6R5g/RhcIRMdljOMG/jx+fUau37E/V4ewwSZjJ0pthvjnVEn3fHP/TbjH2R7aoZJN357fv1Cus2s2KyLYYyRsUoxYN9+llHH7QF2xZajqNl/EnPGDdG0//Vixt9pe+qgblJgGKNk84qd7PlOt5lVUMy6vNpo52e8fFdk23sqo5VigI3j3cKtAfaK772E891RjyiHT7bh0U1HfTPj76Q9Nc84M9lAtq7Y8fNtDTfbLai27V7eS9l4H2e8Usykj+SgKWa/MFdsOdqrECuc7xYY2r8PNi6e5vkXvlP21JkcIpNhksm2FTt+vq3hZrsFzRuVgpf3Urbex64pxURUBmCNEGKqW2XYRS9imVGcWlpQ8inMD6GjM9KraKV72cKsH19F7vtfOoTdx1oBAI9trsfwkr5obgsjP2T8C7Nm/0nN9NuvG2V68HJ6qay0qADfn12FH71wCHkhQqRHWLKnPtF6HiFKjFvstgcPI8Tfy0D67z0mc8mmFTvZilK2LUcbxU3PRkG1bffC25MfyvYSV5RiIioB8DsAhW7kP+X+DWg814mh/fOx454bLOUx6f4NaDrXCQDYePh9LHv5Lew0kFf8gLa9/rSppQWtwVBZohA9AuGIQJ+8HER6BIQQ6JuXayhvmQJoZCCetXwr3o5FfHum9kRvxDeZ3CREygzv8VgM9HC38S/MOeOG4PDJ1F3plYPM30ZGlsrMKs1r6xpx37pDyMshdHZFsHDqhzGlcpBp2Q42nkVHZyQhLX7G2W7YbCsv3fhlsgvdEc17z80XOtsSMpmA3opSNi5HG8VNz0ZBtW330p1etrryc2umOALgNgBrnc54xN0v9v7deK4TI+5+Ee/85GZTedTsbehViBVOnutEzd4G3Rnj+AGtM9KDSE8PuntgaGlBazCMX6JQiP+7LdwtzVumABoZiDcdaupViBWONHfg/nVvYtXu44bk1sPIF+bt143C8o1v40KivogNb72PlvawYUXJyFKZWfsytfr++v/+it/ueAcPzTX+YmtpD+P7aw+mpH//5iqUFhVI5dL6cFGw8tJVWyYDUu89sx+BZmBbQiZT0PLQAyArl6ON4qZno6Datqfb25NfyvYSV5RiIcQ5AKCkJWK7TLl/g2a6mRnjdQeaNNO1lGItxSEeLcVPzzZHbYnCTN4yBdCoXdD6Q82q5T6x411EhLAst4LRL8xnvj4Fc3+5A11xaWaXbF55U90M45U3T6KyrNiSfZlWfcPdPaZebBvebEKPSmT1EMn7UuvDZdOhJsysKrdsAybry7ycHLz53jnXXuhsS8hkGmoeevY1nMnK5Wgz2PVspLfaFFTbdi/d6WWjKz9PgncQ0SIiqiWi2lOnThm+rjFpdleWrsXsseq2slrpwEXFQQ8txU/tWmUwVFuiMJO3nq2UrOx4ZlWVqeaTn6tu+2pUbiAaUtToF+awkr4I5SXKa37JRutjLJouazMtubTqq9aearS0h7Hzr39TPVZ/qkMql9aHi5JutK+TkfVl9JiwlLcRrMptBCt9zTBOUFpUgPEVA3vHvWxdjjZLcrsZZW1dI6Ys24z5K3djyrLNeL6uMeWcyrJizK2uCIxCrGC1TYJethd4ohQLIR4XQlQLIaoHDx5s+Lqh/fNNpWsxZ2IFhiRdM6R/vq7phNqAlhciFOSSNEqQ3mAYH22oIBRV2vrk5SAvRMjNgTRvma2U0YF4ZlU5Rpcl2u5+ZHA/JE9qqsmtyPhvMyrxqaoy5MXkLsglfPuGy/Ha3TMML4U7EX3pxjHqHzdKuhX7MkWugtxUhdvIi00ZsDdoKLafGlMmlUvrw0VJt/rSTW5ztXtvzKUDXHuhq8ndGcluW0Im8+DIcu4Rv9rUFu7Gha7oCh5HHWXMEiiXbDvuuSHBpjg+3Sw777nBlPcJLfsaI0sLMtuc+CUKs94nZLZSZuyCXlk8PWUT1/N1jYbkjpfR7oYpu0s2sjaxal+myPU/u4/jF1vqEzxr6Mkos7+eWlmK6pGlvXJoyaV8uByJM6EYXVbYu9nOjg1YcpsDqfeeW/ZlitzffnYfuiLRz7BITw921J+2bVccVFtCJjPJxuXodJCtnhIY5yEhVAwc00h1dbWora01dY0T3iesYkfh83J3vV/ldhNZm9jxSGCmTfY1nMH8lbt7N64BQN+8EG4ZPwRfuGpYr0JsVC43vE8Yxa28W9rDuOYnmxCO82LSJy8HO5bMcKQcN71PENEeIUS1o5n6GCtjNsO4SUt7GFOWbU6YeFAbP4L6LmOcRW/MDqRSzDBBwuiAnc2ofTgUF+TiqYVXY7zPTR1YKWYY71Fb1YxfaWJ3eIyC3pgdKPMJhgki2eraxgy8CYlhGDvomaZka3Q2xjysFDNMGmBbQn34w4FhGLuUFhWojhlsc8wYhZVihkkTWgM2E4U/HBiGcQNeiWKM4olLNoZhGDWyzScmwzDuw+7wGKPwTDHDMEyWQUSXALgKwBtCiNNey8MwbsMrUYwReKaYYRgmiyCiIQBeBPAJAFuIaDARPUFErxHR9zwWj2Fcw85KVEt7GPsaznBAkAyHZ4oZhmGyizEAFgshdhFRCYAZAEJCiGuI6FdENEoIcdRjGRnGN7A7t+yBZ4oZhmGyCCHExphCPA3R2eIbAayOHd4M4FrPhGMYn8EhpLOLQCrF9c1tWFPbgPrmNltLGvH5WMWJJRUn8nCiLoos294+hW1vv29ZHj+0q5NLXX7pY7180r20l47yksvg5UvnICICcBuALgAEoDF26ByAMpXzFxFRLRHVnjp1Kn2CMr4n059LxZ1bPIo7NybzCJz5xNKaA1i163jv71AOoV9eyPSSRnI+CyYPx723jjUlixNLKk7k4URd6pvb8Nsd7+APfz7e68kxL0T46RfGm5In3e1ae6wF246exrRRg3rDJSvXkxAIRwS+OmUk/n12Vcq1RkJ++qWP4/MJEaEz0oPvzBqNRZ/8iOn87YY81ivPqXDKyWXMu2oYVu85wcuXDiGioUy/QUT3AZgLQPFNVQSVyRIhxOMAHgeiEe3SJSfjb7LBrIDduWUXgQrzXN/chuuXb9M8bjR0rlY+GxdPM/widyJ0rxN5OFGXZEU2noLcHLx2tzF50t2u81fuwvb6lt7fUytL8ejffSzlegCoHNwPG799Xe9vI4O5X/pYKx8AuGX8EKw/1Gw4f7sfLXr1+dnGt21/EGmVkYyfwmQHLcwzES0BcFIIsYqIfg6gFsBgIcTDRPQjAEeEEP+jdT2HeWaA7ApfLwshzQQLvTE7UOYT85/YpXvc6JJGXcMZU+lqOLGk4kQedutS39ymqRAD0Zl4o/Kks11rj7UkKMQA8Gp9Cza82QRS+dCrP/UBNh1qAmDcRswvfazkEyJKSX9+38mUh1grf7W+XrXzuCkzF6367PxLi+289cpIhpcvbfE4gC8T0TYAIQA1sd+PAJiHqGcKhtElm8wKbpkwFDuWzMBTC6/GjiUzWCHOYAKlFDed69Q9bnRJY0LFQFPpajixpOJEHnbrIlNYIz3CsDzpbNdtR9Vdq9af6kA4or76sf5QMwDjg7lf+ljJpzOiPnOanK6VvxMfLVr1Oa1hT6iVt54doloZyfDypXWEEK1CiBuEENOEEP8shDgLYDqAXQCui/1mGE1a2sM4e74TnZFIQrrfn0s79s9BDSyU6TbfThMopfjK8kLNY2Yi1FSWFWPB5OEJaQsmDzdlA+lEhBwn8rBbFz2FNS9EeGiucXnS2a7TRg1Svf5TY8rw1SkjVY/NqoruHzKqqPqlj5V8vjNrtOqxu278qKH8nfho0arPtZXq/aGW99q6RkxZthnzV+7GlGWb8XxdY8JxtTIWTB7O0ahcJKYorxZCNHktC+NvlOf3G0+/gR4B5OYgEM+lbNzJRLKxznYJlE0xAIy4O3Vlb+03pliKUOPEpiAjm7XSkYeduixdewCrdl5c+r7+o4Ox4JoRGHPpAEvypKtdv7xyF15Nsil+cuGkaB1+ugX1pz7oPTa6rBCvLJ7e+9uMjZhf+hgA7lpTh9W1Fwc2xW7XaP7JfW3H7je5PCN5m7FDTC7DqTZ0mqDZFNuFbYqzF7XntyA3B79ZUI0xl/b31XMZTzbZPytkY52NojdmB04pBoDZj27Fm00dGFNeiHXfmu6OYFmGU14D0o2a9wmFTYeasP5QM2ZVlWFmVXnKtX5VsmTY7Ss3+1qW976GM5i/cjfawt29acUFuXhq4dUYb2LG2k+wUsxkC0F9foMqtx2ysc5G0RuzA+eSDQArwi5QWVYcKGVYoXpkaYoyrDCzqlxVGVYoLSoIlDKsYLev3OxrWd7s3ohhgktQn9+gym2HbKyzEwTKpphhmGDjlI01wzDpJ6jPb1DltkM21tkJAmk+wTBMsAmq6YoabD7BZBtBfX6DKrcdsrHOMjLOfIJhmGATVNMVhmGC+/wGVW47ZGOd7cDmEwzDMAzDMEzWw0oxwzAMwzAMk/WwUswwDMMwDMNkPawUMwzDMAzDMFkPK8UMwzAMwzBM1sNKMcMwDMMwDJP1sFLMMAzDMAzDZD2eB+8golMA3rVw6SAApx0Wxwn8KhfgX9n8KhfgX9n8KhfgX9nckusyIcRgF/L1JRk4ZgP+lc2vcgH+lY3lMo9fZUv7mO25UmwVIqr1YxQpv8oF+Fc2v8oF+Fc2v8oF+Fc2v8qVLfi5/f0qm1/lAvwrG8tlHr/K5oVcbD7BMAzDMAzDZD2sFDMMwzAMwzBZT5CV4se9FkADv8oF+Fc2v8oF+Fc2v8oF+Fc2v8qVLfi5/f0qm1/lAvwrG8tlHr/Klna5AmtTzDAMwzAMwzBOEeSZYoZhGIZhGIZxBFaKGYYxDRFdQkQ3ENEgr2VJxs+yMQzDeIVfx0Y/yRVIpZiIniCi14joez6QJZeIjhPR1ti/sUT0IyJ6nYh+4ZFMZUT0auzvPCJaF2uvf9RK80i2oUR0Iq7tBsfS09q/RDSAiP5ERBuI6H+JKF9NBp/IlXCvxc5L6/1GREMAvAjgEwC2ENFgP7SXjmyet1m2w2O2Ibl8OW7zmG1bLl+MP34dt/02ZgdOKSaizwEICSGuAXApEY3yWKRxAH4vhJguhJgOoADAtYh28Akiuj6dwhBRCYDfASiMJX0TQG2svWYTUbFGmheyXQ3gP5S2E0Kc8qh/vwTgESHEDQCaAPxdsgw+ketuxN1rQogDRFSN9N9vYwAsFkL8B4BXAMyAP9pLTbZ/hD/aLGvhMVuOX8dtHrNty+WXMRvw77jtqzE7cEoxgOkAVsf+3oxoQ3nJJACfJaLtRPQ0ojfacyK6g3EjgKlplicC4DYA52K/p+Nie70GoFojzQvZJgH4ZyLaSUTLY2nxsqWlf4UQvxJCbIj9HAxgvooMfpCrG3H3GhHlApiGNN9vQoiNQohdRDQN0UHqRvigvTRkOw8ftFmWMx08Zsvw67jNY7Y9uXwxZsdk8+W47bcxO4hKcSGAxtjf5wCUeSgLALwO4JNCiGsBnAHQFx7KJ4Q4J4Q4G5ek1l6etKGKbH8CcI0QYjKAy4lonFeyAQARTQZQAqBBRQY/yLUBiffaTV7JRUSE6MuyCwCpyOBle8XLtg8+abMsxm/t7asxG/DvuM1jtm25fDNmx+Ty5bjtpzE7iEpxO6KDGAAUwfs67BdCnIz9/Rb8J5+aPH6R8TUhRFvs77cAjIJHshHRJQB+jujSjW/aLEmu5HvNs/YSUb6B6IzVJBUZPLvHkmQr90ubZTF+a2+/j9mAj8agJHjMNieXb8ZswL/jtp/GbD88/GbZg4tT+uMBvOOdKACAJ4loPBGFAHwW0S8aP8mn1l5+acNXiGgIEfVDdCnnoBeyEVE+oktG3xVCvKshgx/kSr7X9nkk1xIiWhD7ORDAT1Rk8OQeU5HtP/3QZlmO39rb72M24JMxSAUes83J5YsxOyabL8dt343ZQohA/QPQP9ZIjwA4DGCAx/JcCWA/gAMA/gPRD40dAH4G4AiAkR7JtTX2/2UA3ozJ8zqAkFqaR7Jdh+iX4H4A/+JV/wL4JwCtALbG/v2/ZBl8ItcP4u+12Dlpv99wcVlwG4BfxdrH8/bSkG2sH9osm//xmG1Ktq2x/301bvOYbVkuX4zZsXJ9OW77bcwOZES72I7YGwBsE0I0eS1PMkTUF8DNAPYKIf7qA3kuRfQr6xURsw9TS/MLfuhfNRn8IJcafrjfgtRegD/aLJvw870A+PN+CNK47Yf+DdIY5Jf7jdtMpZwgKsUMwzAMwzAM4yRBtClmGIZhGIZhGEdhpZhhGIZhGIbJelgpZhiGYRiGYbIeVoqZQEBEj5o494dENN2p8xiGYRjz8LjNBA1WiplAIIT4ltcyMAzDMMbhcZsJGrleC8Aw8RDR64i6XTkAYAKAJ4QQNxHRViHE9Ng5PwSQh6hrogEAPgUgDOBZRP15EqI+ItXyL0k+L+aIfhWADwE4IIT4Rsz9yx8BlAL4Syz9fudrzDAME2x43GYyBZ4pZvzGMUQjJf0ZwCwAezXOqxRCfBLA/wCYAWARgHVCiOsQjZ+uhdp5iwAcFEJMAzCEiMYB+CiAEwCmAPgID6wMwzCa8LjNZASsFDN+Yy+AeQBeBPAFRMM7qrEq9v/7APIBjEQ0Cg4A1Orkr3beaACfJaKtAD4MYCiARgBXIRpl52dmK8EwDJNF8LjNZASsFDN+4w1EQ4luQHTmQWvGoSPp97sAqmJ/T9DJX+28IwAejS3zfQ/AcUSX9u4TQkwWQjxtXHyGYZisg8dtJiNgpZjxG3sRHdyOAXhfCPGuwet+A+DzsVmD/ibP+w2ATxPRNgBfB9CA6CD/cyLaTER/IKIrTdeEYRgmO+Bxm8kIOMwzw6hARF8D8EVE7de6ADwshNjqqVAMwzCMJjxuM3ZhpZjJWGKzCvGcFULc6oUsDMMwjBwetxkvYaWYYRiGYRiGyXrYpphhGIZhGIbJelgpZhiGYRiGYbIeVooZhmEYhmGYrIeVYoZhGIZhGCbrYaWYYRiGYRiGyXr+P6eVnVP/qvSeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2*6, 5))\n",
    "axes1 = plt.subplot(1, 2, 1)\n",
    "cities.plot(x='wind_deg', y='wind_speed', kind='scatter', ax=axes1)  # ax 参数可以指定绘制哪个子图z\n",
    "axes1.set_title('wind degree and wind speed')\n",
    "\n",
    "axes2 = plt.subplot(1, 2, 2)\n",
    "cities.plot(x='wind_deg', y='humidity', kind='scatter', ax=axes2)\n",
    "axes2.set_title('wind degree and humidity')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd53a6b-b7b3-42cf-8980-4e33f5781c68",
   "metadata": {},
   "source": [
    "可以看到散点图的观察不出什么关系来，由于风向是360度的，我们可以考虑使用极坐标系条形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "46098727-1d9a-4dff-8d7b-7e75c06b30c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PolarAxesSubplot:xlabel='wind_deg'>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plt.subplot(projection='polar', facecolor='g')\n",
    "cities.plot(x='wind_deg', y='wind_speed', kind='bar', ax=axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "065b743e-193a-4b62-8aa7-0b6e8c21d806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='wind_deg'>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities.plot(x='wind_deg', y='wind_speed', kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ebf64ae1-a952-4782-99a8-3bfd2536e796",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.7586507936507936"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 每个方向都对应一个风速，675个风速，太多了，画出来很丑，不利于观察\n",
    "# 把风速整合到八个区间，计算个个区间的平均风速\n",
    "cities.loc[(cities.wind_deg < 45) & (cities.wind_deg >= 0), 'wind_speed'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "55d59f51-9fa7-4894-9d5f-abd2383da4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936,\n",
       " 1.7586507936507936]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 向上面这样一个个计算太费神，我们写一个for循环一次性算出来\n",
    "speeds = []\n",
    "for degree in range(45, 361, 45):\n",
    "    speed = cities.loc[(cities.wind_deg < 45) & (cities.wind_deg >= 0), 'wind_speed'].mean()\n",
    "    speeds.append(speed)\n",
    "\n",
    "speeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "8000ceb3-8807-4ed5-aaf7-55e4b3c45dac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出这个极坐标条形图\n",
    "axes = plt.subplot(projection='polar', facecolor='g')\n",
    "axes.bar(list(range(45, 361, 45)), speeds, color=np.random.rand(8, 3), width=np.pi/4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d99335e-0883-4750-ac91-d194657463db",
   "metadata": {},
   "source": [
    "计算米兰各个方向上的风速"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3d3ab396-c45f-4df6-9771-a4afd2e51668",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>pressure</th>\n",
       "      <th>description</th>\n",
       "      <th>dt</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_deg</th>\n",
       "      <th>city</th>\n",
       "      <th>day</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.57</td>\n",
       "      <td>54</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436863175</td>\n",
       "      <td>2.1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-14 10:39:35</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.74</td>\n",
       "      <td>48</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436866758</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-14 11:39:18</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.12</td>\n",
       "      <td>48</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436870509</td>\n",
       "      <td>2.6</td>\n",
       "      <td>140.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-14 12:41:49</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.16</td>\n",
       "      <td>45</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436874098</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-14 13:41:38</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.59</td>\n",
       "      <td>43</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>Sky is Clear</td>\n",
       "      <td>1436877644</td>\n",
       "      <td>3.1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Milano</td>\n",
       "      <td>2015-07-14 14:40:44</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    temp  humidity  pressure   description          dt  wind_speed  wind_deg  \\\n",
       "0  28.57        54    1016.0  Sky is Clear  1436863175         2.1     100.0   \n",
       "1  29.74        48    1016.0  Sky is Clear  1436866758         2.6       0.0   \n",
       "2  31.12        48    1016.0  Sky is Clear  1436870509         2.6     140.0   \n",
       "3  32.16        45    1015.0  Sky is Clear  1436874098         2.1       0.0   \n",
       "4  33.59        43    1015.0  Sky is Clear  1436877644         3.1      80.0   \n",
       "\n",
       "     city                  day  dist  \n",
       "0  Milano  2015-07-14 10:39:35   250  \n",
       "1  Milano  2015-07-14 11:39:18   250  \n",
       "2  Milano  2015-07-14 12:41:49   250  \n",
       "3  Milano  2015-07-14 13:41:38   250  \n",
       "4  Milano  2015-07-14 14:40:44   250  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milano = cities.loc[cities.city == 'Milano']\n",
    "milano.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6f891842-0a51-4cda-9f93-7c160e6ff099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8142857142857143\n",
      "2.2222222222222223\n",
      "2.855555555555556\n",
      "2.583333333333333\n",
      "2.3285714285714287\n",
      "2.266666666666667\n",
      "2.05\n",
      "2.1\n"
     ]
    }
   ],
   "source": [
    "speeds = []\n",
    "for degree in range(45, 361, 45):\n",
    "    speed = milano.loc[(milano.wind_deg < degree)  & (milano.wind_deg >= (degree - 45)), 'wind_speed'].mean()\n",
    "    print(speed)\n",
    "    speeds.append(speed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "1119524b-c74d-40e8-a613-8ce355282735",
   "metadata": {},
   "outputs": [],
   "source": [
    "deg_range = np.arange(0, 2*np.pi, 2*np.pi/8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "c5829711-8b42-4a1e-be80-24e8371147b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Milano')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(projection='polar', facecolor='g')\n",
    "plt.bar(deg_range, speeds, color=np.random.rand(8, 3), width=np.pi/4)\n",
    "plt.title('Milano')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b9518838-ff03-4bd8-941c-42357edeab54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan is None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e01f315-fbff-41b3-bfcd-b8cf55eaa3d2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## 3、线性回归预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eb6fc14a-17df-48c3-b5fb-888f3bac50f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b8dbdedd-8960-43cc-8a2e-6d78f5cfef25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.85, 32.81, 32.74, 33.43, 32.79])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([34.31, 33.85, 34.18, 34.81, 33.92, 34.69])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_near = temp_max[cond_near]\n",
    "temp_far = temp_max[cond_far]\n",
    "display(temp_near, temp_far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "88aeee7f-f10a-4962-a189-880b3ac3f88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 线性回归就是用来预测线性趋势\n",
    "# 机器学习的代码特别简单\n",
    "# 准备好数据\n",
    "# 机器学习中，训练数据必须是二维的\n",
    "# 创建模型\n",
    "# 训练\n",
    "# 预测\n",
    "linear_near = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "815da816-5d38-44f1-800a-d88b82a0a8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练模型\n",
    "linear_near.fit(dist_near.reshape(-1, 1), temp_near)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a7ace6-bdfe-456d-bec1-c3a90e5ba7c0",
   "metadata": {},
   "source": [
    "coef_是一个数组，其长度等于你模型中特征的数量（不包括截距项）。对于每个特征，coef_数组中的相应元素表示该特征的系数。这些系数描述了当其他特征保持不变时，该特征的值增加一个单位时，目标变量（即你试图预测的变量）的预期变化量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b133a5d7-3c29-4d58-abbf-f45e30c032b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0170871])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_near.coef_  # w "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0277068c-2897-4d40-9f50-eca2a652b515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32.51911679167305"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_near.intercept_  # b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1bc64986-d7ad-4c32-91f5-956e55bde776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 再训练一个far的模型\n",
    "linear_far = LinearRegression()\n",
    "linear_far.fit(dist_far.reshape(-1, 1), temp_far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "33e69adc-d523-4113-a29d-3f58bc91f1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32.51911679, 32.70897342, 32.89883004, 33.08868667, 33.27854329,\n",
       "       33.46839992, 33.65825654, 33.84811317, 34.03796979, 34.22782642])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型预测\n",
    "x_test_near = np.linspace(0, 100, 10).reshape(-1, 1)\n",
    "y_near = linear_near.predict(x_test_near)\n",
    "y_near"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "964a29a9-b8dd-4665-ac62-9b6563c7f1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.98912266, 34.06013356, 34.13114445, 34.20215534, 34.27316624,\n",
       "       34.34417713, 34.41518803, 34.48619892, 34.55720982, 34.62822071])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test_far = np.linspace(100, 350, 10).reshape(-1, 1)\n",
    "y_far = linear_far.predict(x_test_far)\n",
    "y_far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "84544509-692c-44e6-8011-267e500ba4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist, temp_max)\n",
    "plt.plot(x_test_near, y_near, x_test_far, y_far, c='r')\n",
    "plt.title('离海岸线的距离和最高温度之间的关系\\n', fontsize=16)\n",
    "plt.xlabel('距离')\n",
    "plt.ylabel('温度')\n",
    "plt.text(25, 34, '近海', c='r')\n",
    "plt.text(200, 34.5, '远海', c='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "fcdc50d9-da1d-4bdb-a5f1-75f479645cb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '最低温与距离海岸远近的关系\\n')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看最低温度与距离海岸远近的关系\n",
    "plt.scatter(dist, temp_min)\n",
    "plt.title('最低温与距离海岸远近的关系\\n', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f2264693-5669-4499-87ca-3c63016b4a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24a7d6e8340>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看最低湿度与距离海岸线远近的关系\n",
    "plt.scatter(dist, humidity_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "263243e2-14fe-4400-961c-dc5d70761a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24a7dc80340>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看最高湿度与距离海岸线远近的关系\n",
    "plt.scatter(dist, humidity_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "00215107-14f3-4d10-875e-bd089cca05c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63.13235294, 61.33823529, 77.64705882, 81.07462687, 61.30882353,\n",
       "       59.14705882, 56.98484848, 59.30882353, 68.01515152, 63.19117647])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均湿度与距离海岸线远近的关系\n",
    "humidity_mean = cities.groupby(by='city')['humidity'].mean().values\n",
    "humidity_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f001bd4e-6102-40d1-94f6-fad81d96737e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x24a7e3e8af0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dWVbJ+BvAq8AUcD+tP3T/CvwNMA38at0Zl+V9rvrv1cB/VDlfBvrajTUo7020nrG8CvxJk/YP4G7gHeC56usPl+dqcNa/WLz/VvdpzD7MheWc54GHqrls5NyukHdXXfNb6ycnq3cXHAKez8y3aguyDhExAPw28O+Z+dO686wkIoZp/eWfyGo9ud1YkzV1/2iXq6lZ22nyPlz63MLmzK8feZekwjTpAJUkqQMWtyQVxuKWpMJY3JJUGItbkgrzfwiFrmaYXVwVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dist, humidity_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d972c9a0-301b-4c68-91f8-0e7e2dfb2925",
   "metadata": {},
   "source": [
    "平均湿度与距离海岸线远近的回归曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "913be8a4-953e-49f0-9d48-37c6f423bf5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([77.64705882, 81.07462687, 61.30882353, 68.01515152])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_mean_near = humidity_mean[dist < 50]\n",
    "humidity_mean_near"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "83e9a909-1102-4764-8da9-c0332bc99d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([63.13235294, 61.33823529, 59.14705882, 56.98484848, 59.30882353,\n",
       "       63.19117647])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_mean_far = humidity_mean[dist > 50]\n",
    "humidity_mean_far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "dc072b04-390e-4740-8983-70a77386a3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 近海地区的湿度预测\n",
    "linear_humidity_near = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ca2ae77a-78b6-4745-ad71-ea6585dd2fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14, 37, 47,  8], dtype=int64)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist[dist < 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "79b95abd-d97f-4900-b1f6-6b08a1d2cf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_humidity_near.fit(dist[dist < 50].reshape(-1, 1), humidity_mean_near)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "afe516fa-cb51-4c76-8a59-8c9bec4cf2e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_near = np.linspace(0, 50, num=10).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "30a714ed-7ce8-4455-8a7f-58b9c1bf6203",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_near = linear_humidity_near.predict(X_test_near)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bf46ea5a-2eb6-4b8e-9623-1cf43e0e7bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 远海地区的湿度预测\n",
    "linear_humidity_far = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "97418f6d-759f-49ca-9935-8bd66b56e3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_humidity_far.fit(dist[dist >= 50].reshape(-1, 1), humidity_mean_far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "44db4b43-8ab2-450f-96d7-16ddc420963a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_far = np.linspace(50, 350, num=10).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "87258d70-683b-463a-ae11-6c225878c7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_far = linear_humidity_far.predict(X_test_far)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "7c895507-e548-4c5e-b20c-cb59addc3765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71, 14, 37, 47,  8], dtype=int64)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_near"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "00aa57fb-b29b-45e3-8398-e8ada572ad4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化展示预测结果\n",
    "plt.scatter(dist, humidity_mean)\n",
    "plt.plot(X_test_near, y_pred_near, X_test_far, y_pred_far, c='c')\n",
    "\n",
    "plt.title('距离海岸线距离的湿度预测\\n', fontsize=16)\n",
    "plt.xlabel('距离', loc='right')\n",
    "plt.ylabel('湿度', loc='top', rotation=0)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cccb7176-e218-46d2-98fb-6fbb47c8b0bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
